Vector-Quantized Variational Autoencoders.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Zs1k05EgEW-A"
      },
      "outputs": [],
      "source": [
        "!pip install -q tensorflow-probability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "HEWdR9DrEW-B"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow_probability as tfp\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "MWlOkQVEEW-C"
      },
      "outputs": [],
      "source": [
        "class VectorQuantizer(layers.Layer):\n",
        "    def __init__(self, num_embeddings, embedding_dim, beta=0.25, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.num_embeddings = num_embeddings\n",
        "        self.beta = (\n",
        "            beta  # This parameter is best kept between [0.25, 2] as per the paper.\n",
        "        )\n",
        "\n",
        "        # Initialize the embeddings which we will quantize.\n",
        "        w_init = tf.random_uniform_initializer()\n",
        "        self.embeddings = tf.Variable(\n",
        "            initial_value=w_init(\n",
        "                shape=(self.embedding_dim, self.num_embeddings), dtype=\"float32\"\n",
        "            ),\n",
        "            trainable=True,\n",
        "            name=\"embeddings_vqvae\",\n",
        "        )\n",
        "\n",
        "    def call(self, x):\n",
        "        # Calculate the input shape of the inputs and\n",
        "        # then flatten the inputs keeping `embedding_dim` intact.\n",
        "        input_shape = tf.shape(x)\n",
        "        flattened = tf.reshape(x, [-1, self.embedding_dim])\n",
        "\n",
        "        # Quantization.\n",
        "        encoding_indices = self.get_code_indices(flattened)\n",
        "        encodings = tf.one_hot(encoding_indices, self.num_embeddings)\n",
        "        quantized = tf.matmul(encodings, self.embeddings, transpose_b=True)\n",
        "        quantized = tf.reshape(quantized, input_shape)\n",
        "\n",
        "        # Calculate vector quantization loss and add that to the layer. You can learn more\n",
        "        # about adding losses to different layers here:\n",
        "        # https://keras.io/guides/making_new_layers_and_models_via_subclassing/. Check\n",
        "        # the original paper to get a handle on the formulation of the loss function.\n",
        "        commitment_loss = self.beta * tf.reduce_mean(\n",
        "            (tf.stop_gradient(quantized) - x) ** 2\n",
        "        )\n",
        "        codebook_loss = tf.reduce_mean((quantized - tf.stop_gradient(x)) ** 2)\n",
        "        self.add_loss(commitment_loss + codebook_loss)\n",
        "\n",
        "        # Straight-through estimator.\n",
        "        quantized = x + tf.stop_gradient(quantized - x)\n",
        "        return quantized\n",
        "\n",
        "    def get_code_indices(self, flattened_inputs):\n",
        "        # Calculate L2-normalized distance between the inputs and the codes.\n",
        "        similarity = tf.matmul(flattened_inputs, self.embeddings)\n",
        "        distances = (\n",
        "            tf.reduce_sum(flattened_inputs ** 2, axis=1, keepdims=True)\n",
        "            + tf.reduce_sum(self.embeddings ** 2, axis=0)\n",
        "            - 2 * similarity\n",
        "        )\n",
        "\n",
        "        # Derive the indices for minimum distances.\n",
        "        encoding_indices = tf.argmin(distances, axis=1)\n",
        "        return encoding_indices\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "NTQIeG0CEW-E"
      },
      "outputs": [],
      "source": [
        "def get_encoder(latent_dim=16):\n",
        "    encoder_inputs = keras.Input(shape=(100, 100, 3))\n",
        "    x = layers.Conv2D(32, 3, activation=\"relu\", strides=2, padding=\"same\")(\n",
        "        encoder_inputs\n",
        "    )\n",
        "    x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
        "    encoder_outputs = layers.Conv2D(latent_dim, 1, padding=\"same\")(x)\n",
        "    return keras.Model(encoder_inputs, encoder_outputs, name=\"encoder\")\n",
        "\n",
        "\n",
        "def get_decoder(latent_dim=16):\n",
        "    latent_inputs = keras.Input(shape=get_encoder(latent_dim).output.shape[1:])\n",
        "    x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(\n",
        "        latent_inputs\n",
        "    )\n",
        "    x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
        "    decoder_outputs = layers.Conv2DTranspose(3, 3, padding=\"same\")(x)\n",
        "    return keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_decoder().summary()"
      ],
      "metadata": {
        "id": "PQaM-1LHvSbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLkfsSKaEW-F"
      },
      "source": [
        "## Standalone VQ-VAE model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kYGuEWmGEW-F"
      },
      "outputs": [],
      "source": [
        "def get_vqvae(latent_dim=16, num_embeddings=64):\n",
        "    vq_layer = VectorQuantizer(num_embeddings, latent_dim, name=\"vector_quantizer\")\n",
        "    encoder = get_encoder(latent_dim)\n",
        "    decoder = get_decoder(latent_dim)\n",
        "    inputs = keras.Input(shape=(100, 100, 3))\n",
        "    encoder_outputs = encoder(inputs)\n",
        "    quantized_latents = vq_layer(encoder_outputs)\n",
        "    reconstructions = decoder(quantized_latents)\n",
        "    return keras.Model(inputs, reconstructions, name=\"vq_vae\")\n",
        "\n",
        "\n",
        "get_vqvae().summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "scHhfEXrEW-G"
      },
      "outputs": [],
      "source": [
        "from tensorflow.python.ops.gen_data_flow_ops import map_stage\n",
        "class VQVAETrainer(keras.models.Model):\n",
        "\n",
        "        \n",
        "    def __init__(self, train_variance, latent_dim=32, num_embeddings=128, **kwargs):\n",
        "        super(VQVAETrainer, self).__init__(**kwargs)\n",
        "        self.train_variance = train_variance\n",
        "        self.latent_dim = latent_dim\n",
        "        self.num_embeddings = num_embeddings\n",
        "\n",
        "        self.vqvae = get_vqvae(self.latent_dim, self.num_embeddings)\n",
        "\n",
        "        self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n",
        "        self.reconstruction_loss_tracker = keras.metrics.Mean(\n",
        "            name=\"reconstruction_loss\"\n",
        "        )\n",
        "        self.vq_loss_tracker = keras.metrics.Mean(name=\"vq_loss\")\n",
        "        \n",
        "    \n",
        "    \n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [\n",
        "            self.total_loss_tracker,\n",
        "            self.reconstruction_loss_tracker,\n",
        "            self.vq_loss_tracker,\n",
        "        ]\n",
        "\n",
        "    def train_step(self, data):\n",
        "        with tf.GradientTape() as tape:\n",
        "            x,y = data\n",
        "            # print(y.dtype)\n",
        "            # print(x.dtype)\n",
        "            x = tf.cast(x,tf.float32)\n",
        "            y = tf.cast(y,tf.float32)\n",
        "            # Outputs from the VQ-VAE.\n",
        "            reconstructions = self.vqvae(x)\n",
        "\n",
        "            # Calculate the losses.\n",
        "            reconstruction_loss = (\n",
        "                tf.reduce_mean((y - reconstructions) ** 2) / self.train_variance\n",
        "            )\n",
        "            total_loss = reconstruction_loss + sum(self.vqvae.losses)\n",
        "\n",
        "        # Backpropagation.\n",
        "        grads = tape.gradient(total_loss, self.vqvae.trainable_variables)\n",
        "        self.optimizer.apply_gradients(zip(grads, self.vqvae.trainable_variables))\n",
        "\n",
        "        # Loss tracking.\n",
        "        self.total_loss_tracker.update_state(total_loss)\n",
        "        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
        "        self.vq_loss_tracker.update_state(sum(self.vqvae.losses))\n",
        "\n",
        "        # Log results.\n",
        "        return {\n",
        "            \"loss\": self.total_loss_tracker.result(),\n",
        "            \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
        "            \"vqvae_loss\": self.vq_loss_tracker.result(),\n",
        "        }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQ0IbXCCEW-G"
      },
      "source": [
        "## Load and preprocess the MNIST dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Download Dataset ***`Don't Execute`***"
      ],
      "metadata": {
        "id": "3A0vhi_4PwL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "ZL2MQVbGFymi"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/drive/MyDrive/Kaggle\""
      ],
      "metadata": {
        "id": "-5PL7Q1PPyiw"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d prondeau/the-car-connection-picture-dataset"
      ],
      "metadata": {
        "id": "BfzdfueKoTv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/the-car-connection-picture-dataset.zip' -d '/content/data'"
      ],
      "metadata": {
        "id": "7ftEha1wP5yi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/data/'"
      ],
      "metadata": {
        "id": "V7XDjBc6Wxxt"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "files = os.listdir(path)"
      ],
      "metadata": {
        "id": "zh-WSTG5Jd9U"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "files = files[:len(files) // 10] #Because The Google Colab's RAM Is not large \n",
        "                                #So We Take Only The First Half Of The Dataset"
      ],
      "metadata": {
        "id": "_H5zFm1nJoIl"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Add Some Motion Blur Effect\n",
        "kernel_size = 10\n",
        "kernel_v = np.zeros((kernel_size, kernel_size))\n",
        "kernel_v[:, int((kernel_size - 1)/2)] = np.ones(kernel_size)\n",
        "kernel_v /= kernel_size"
      ],
      "metadata": {
        "id": "eMX2aKSTZBM7"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DATASET_SIZE = len(files)\n",
        "IMG_HEIGHT = 100\n",
        "IMG_WIDTH = 100"
      ],
      "metadata": {
        "id": "Nx9VcNSFohUo"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2"
      ],
      "metadata": {
        "id": "dKNV94SdGemw"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.zeros((DATASET_SIZE,IMG_HEIGHT,IMG_WIDTH,3))\n",
        "y = np.zeros((DATASET_SIZE,IMG_HEIGHT,IMG_WIDTH,3))\n",
        "for index , filename in enumerate(files):\n",
        "  img = cv2.imread(path + filename)\n",
        "  img = cv2.resize(img, (IMG_HEIGHT,IMG_WIDTH)) / 255\n",
        "  X[index] = cv2.filter2D(img, -1, kernel_v)\n",
        "  y[index] = img"
      ],
      "metadata": {
        "id": "PmNsk7oXXMfX"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "YIZupAgiEW-G"
      },
      "outputs": [],
      "source": [
        "# (x_train, _), (x_test, _) = keras.datasets.mnist.load_data()\n",
        "# print(x_train.shape)\n",
        "# x_train = np.expand_dims(x_train, -1)\n",
        "# x_test = np.expand_dims(x_test, -1)\n",
        "x_train_scaled = X - 0.5\n",
        "\n",
        "data_variance = np.var(x_train_scaled / 255.0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tR8eJSpdEW-G"
      },
      "source": [
        "## Train the VQ-VAE model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "X0xqKBdoEW-G",
        "outputId": "058f53cc-8f71-4aec-cb60-9a00609f1a21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1428178.5577 - reconstruction_loss: 13710.3760 - vqvae_loss: 1414618.1514\n",
            "Epoch 840/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1425281.1755 - reconstruction_loss: 13696.6406 - vqvae_loss: 1411628.4038\n",
            "Epoch 841/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1397780.9615 - reconstruction_loss: 13720.0254 - vqvae_loss: 1384037.5192\n",
            "Epoch 842/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1399701.2716 - reconstruction_loss: 13709.1377 - vqvae_loss: 1386002.1562\n",
            "Epoch 843/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1416629.8197 - reconstruction_loss: 13721.0430 - vqvae_loss: 1402881.2692\n",
            "Epoch 844/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1404297.4880 - reconstruction_loss: 13677.7939 - vqvae_loss: 1390592.8077\n",
            "Epoch 845/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1374201.8413 - reconstruction_loss: 13680.7725 - vqvae_loss: 1360592.2668\n",
            "Epoch 846/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1404710.2356 - reconstruction_loss: 13687.3828 - vqvae_loss: 1390951.4519\n",
            "Epoch 847/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1384090.9591 - reconstruction_loss: 13707.4570 - vqvae_loss: 1370437.1010\n",
            "Epoch 848/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1428917.1034 - reconstruction_loss: 13728.8604 - vqvae_loss: 1415214.7812\n",
            "Epoch 849/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1392088.7572 - reconstruction_loss: 13659.3848 - vqvae_loss: 1378446.2212\n",
            "Epoch 850/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1359529.3750 - reconstruction_loss: 13663.4561 - vqvae_loss: 1345729.4038\n",
            "Epoch 851/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1358925.2692 - reconstruction_loss: 13661.8652 - vqvae_loss: 1345280.8413\n",
            "Epoch 852/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1351363.6034 - reconstruction_loss: 13638.8350 - vqvae_loss: 1337756.7885\n",
            "Epoch 853/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1343965.4976 - reconstruction_loss: 13657.7607 - vqvae_loss: 1330205.1274\n",
            "Epoch 854/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1312265.1707 - reconstruction_loss: 13680.7725 - vqvae_loss: 1298715.4159\n",
            "Epoch 855/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1335200.6370 - reconstruction_loss: 13649.2529 - vqvae_loss: 1321635.9856\n",
            "Epoch 856/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1342217.8389 - reconstruction_loss: 13683.0479 - vqvae_loss: 1328584.9760\n",
            "Epoch 857/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1349773.8269 - reconstruction_loss: 13690.3066 - vqvae_loss: 1336214.9976\n",
            "Epoch 858/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1328404.5288 - reconstruction_loss: 13732.7734 - vqvae_loss: 1314578.0889\n",
            "Epoch 859/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1325741.7764 - reconstruction_loss: 13774.8447 - vqvae_loss: 1311941.6130\n",
            "Epoch 860/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1406565.0240 - reconstruction_loss: 13862.7412 - vqvae_loss: 1392789.0288\n",
            "Epoch 861/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1371962.2909 - reconstruction_loss: 13784.5996 - vqvae_loss: 1358246.8173\n",
            "Epoch 862/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1350222.7764 - reconstruction_loss: 13871.0234 - vqvae_loss: 1336352.9087\n",
            "Epoch 863/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1379979.5312 - reconstruction_loss: 13974.1543 - vqvae_loss: 1365978.7524\n",
            "Epoch 864/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1391019.7716 - reconstruction_loss: 13974.6094 - vqvae_loss: 1377191.2284\n",
            "Epoch 865/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1449758.5697 - reconstruction_loss: 14016.0908 - vqvae_loss: 1435615.6226\n",
            "Epoch 866/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1429095.9784 - reconstruction_loss: 14036.0244 - vqvae_loss: 1415070.3438\n",
            "Epoch 867/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1502368.7163 - reconstruction_loss: 14474.5029 - vqvae_loss: 1487999.2788\n",
            "Epoch 868/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1686556.4279 - reconstruction_loss: 15121.2930 - vqvae_loss: 1671599.5168\n",
            "Epoch 869/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 1528331.8029 - reconstruction_loss: 14345.2734 - vqvae_loss: 1513892.6106\n",
            "Epoch 870/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1451537.5601 - reconstruction_loss: 14165.4268 - vqvae_loss: 1437314.8654\n",
            "Epoch 871/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1505820.0721 - reconstruction_loss: 14130.7275 - vqvae_loss: 1491616.3413\n",
            "Epoch 872/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1532976.6562 - reconstruction_loss: 14016.5684 - vqvae_loss: 1518968.7188\n",
            "Epoch 873/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1551876.7524 - reconstruction_loss: 14013.8662 - vqvae_loss: 1537901.2500\n",
            "Epoch 874/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1583562.5913 - reconstruction_loss: 13934.4902 - vqvae_loss: 1569752.5048\n",
            "Epoch 875/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1609550.7332 - reconstruction_loss: 13955.3975 - vqvae_loss: 1595642.1490\n",
            "Epoch 876/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1605702.1635 - reconstruction_loss: 14036.0244 - vqvae_loss: 1591887.7043\n",
            "Epoch 877/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1644189.1971 - reconstruction_loss: 14027.5947 - vqvae_loss: 1630062.3149\n",
            "Epoch 878/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1679320.2933 - reconstruction_loss: 14051.0967 - vqvae_loss: 1665245.3582\n",
            "Epoch 879/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1692059.2740 - reconstruction_loss: 14008.5908 - vqvae_loss: 1678064.3534\n",
            "Epoch 880/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1659101.2236 - reconstruction_loss: 14001.6006 - vqvae_loss: 1645091.2115\n",
            "Epoch 881/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1637699.8389 - reconstruction_loss: 13993.5098 - vqvae_loss: 1623639.6923\n",
            "Epoch 882/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1623924.7067 - reconstruction_loss: 13973.6055 - vqvae_loss: 1609932.9303\n",
            "Epoch 883/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1635212.2115 - reconstruction_loss: 13997.6602 - vqvae_loss: 1621311.6659\n",
            "Epoch 884/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1639821.9736 - reconstruction_loss: 14070.7832 - vqvae_loss: 1625752.9639\n",
            "Epoch 885/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1684185.6034 - reconstruction_loss: 14120.8135 - vqvae_loss: 1670012.2572\n",
            "Epoch 886/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1686602.3365 - reconstruction_loss: 14236.1543 - vqvae_loss: 1672433.7788\n",
            "Epoch 887/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1724097.9904 - reconstruction_loss: 14427.4717 - vqvae_loss: 1709804.5769\n",
            "Epoch 888/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1869302.0457 - reconstruction_loss: 15678.4434 - vqvae_loss: 1854382.2716\n",
            "Epoch 889/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2792532.8125 - reconstruction_loss: 16910.2500 - vqvae_loss: 2775783.4615\n",
            "Epoch 890/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 2245555.3894 - reconstruction_loss: 15494.3848 - vqvae_loss: 2229798.1250\n",
            "Epoch 891/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1894669.6466 - reconstruction_loss: 14849.4365 - vqvae_loss: 1879719.6827\n",
            "Epoch 892/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1773751.8413 - reconstruction_loss: 14616.8086 - vqvae_loss: 1759116.7981\n",
            "Epoch 893/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1669810.9928 - reconstruction_loss: 14387.7656 - vqvae_loss: 1655386.2476\n",
            "Epoch 894/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1612867.2885 - reconstruction_loss: 14279.6973 - vqvae_loss: 1598564.1274\n",
            "Epoch 895/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1559364.0072 - reconstruction_loss: 14211.8975 - vqvae_loss: 1545140.2572\n",
            "Epoch 896/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1515152.8558 - reconstruction_loss: 14190.6240 - vqvae_loss: 1500946.7332\n",
            "Epoch 897/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1501819.9279 - reconstruction_loss: 14217.2021 - vqvae_loss: 1487426.3606\n",
            "Epoch 898/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1511655.4327 - reconstruction_loss: 14241.1094 - vqvae_loss: 1497421.2764\n",
            "Epoch 899/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1519685.8077 - reconstruction_loss: 14251.9082 - vqvae_loss: 1505465.6707\n",
            "Epoch 900/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1512023.3293 - reconstruction_loss: 14278.6748 - vqvae_loss: 1497848.1178\n",
            "Epoch 901/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1517856.4880 - reconstruction_loss: 14295.6025 - vqvae_loss: 1503559.4639\n",
            "Epoch 902/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1563441.7933 - reconstruction_loss: 14319.1904 - vqvae_loss: 1549177.4135\n",
            "Epoch 903/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1584784.8894 - reconstruction_loss: 14375.0059 - vqvae_loss: 1570245.3966\n",
            "Epoch 904/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1606451.2236 - reconstruction_loss: 14414.5254 - vqvae_loss: 1591939.5889\n",
            "Epoch 905/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1571785.8125 - reconstruction_loss: 14415.1377 - vqvae_loss: 1557414.6298\n",
            "Epoch 906/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1577215.6923 - reconstruction_loss: 14509.4053 - vqvae_loss: 1562714.6731\n",
            "Epoch 907/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1611523.8630 - reconstruction_loss: 14461.9990 - vqvae_loss: 1597140.0721\n",
            "Epoch 908/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1620359.3317 - reconstruction_loss: 14516.6924 - vqvae_loss: 1605968.0096\n",
            "Epoch 909/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1662418.6731 - reconstruction_loss: 14593.6123 - vqvae_loss: 1647780.0433\n",
            "Epoch 910/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1686721.4591 - reconstruction_loss: 14668.8623 - vqvae_loss: 1672122.6875\n",
            "Epoch 911/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1753495.7596 - reconstruction_loss: 14703.0625 - vqvae_loss: 1738780.6899\n",
            "Epoch 912/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1792135.6034 - reconstruction_loss: 14706.2266 - vqvae_loss: 1777432.7284\n",
            "Epoch 913/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1807229.1250 - reconstruction_loss: 14694.8271 - vqvae_loss: 1792567.4327\n",
            "Epoch 914/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1785213.5361 - reconstruction_loss: 14808.8857 - vqvae_loss: 1770391.0457\n",
            "Epoch 915/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1758647.3389 - reconstruction_loss: 14817.2012 - vqvae_loss: 1743727.2163\n",
            "Epoch 916/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1823748.7668 - reconstruction_loss: 14796.7920 - vqvae_loss: 1809022.8341\n",
            "Epoch 917/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 1865887.9014 - reconstruction_loss: 14701.7295 - vqvae_loss: 1851185.8293\n",
            "Epoch 918/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1813977.9519 - reconstruction_loss: 14686.6260 - vqvae_loss: 1799280.7740\n",
            "Epoch 919/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1761146.2043 - reconstruction_loss: 14595.8945 - vqvae_loss: 1746438.4327\n",
            "Epoch 920/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1755898.2452 - reconstruction_loss: 14588.5469 - vqvae_loss: 1741140.6298\n",
            "Epoch 921/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1729532.7452 - reconstruction_loss: 14616.8936 - vqvae_loss: 1714905.7812\n",
            "Epoch 922/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1771048.5361 - reconstruction_loss: 14593.9316 - vqvae_loss: 1756399.7764\n",
            "Epoch 923/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1808307.9784 - reconstruction_loss: 14570.5352 - vqvae_loss: 1793789.5024\n",
            "Epoch 924/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1816823.6538 - reconstruction_loss: 14523.1045 - vqvae_loss: 1802277.4062\n",
            "Epoch 925/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1829539.6274 - reconstruction_loss: 14535.1094 - vqvae_loss: 1815039.7452\n",
            "Epoch 926/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1764050.2236 - reconstruction_loss: 14561.9082 - vqvae_loss: 1749523.3654\n",
            "Epoch 927/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1730003.8894 - reconstruction_loss: 14538.9658 - vqvae_loss: 1715402.8510\n",
            "Epoch 928/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1712053.7692 - reconstruction_loss: 14502.7998 - vqvae_loss: 1697435.4688\n",
            "Epoch 929/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1721742.7428 - reconstruction_loss: 14456.3066 - vqvae_loss: 1707089.1250\n",
            "Epoch 930/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1710260.1875 - reconstruction_loss: 14401.4336 - vqvae_loss: 1695881.6562\n",
            "Epoch 931/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1747136.6755 - reconstruction_loss: 14395.7637 - vqvae_loss: 1732722.2356\n",
            "Epoch 932/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1738347.4712 - reconstruction_loss: 14402.6572 - vqvae_loss: 1723972.6611\n",
            "Epoch 933/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1717740.4567 - reconstruction_loss: 14393.5703 - vqvae_loss: 1703392.2332\n",
            "Epoch 934/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1726553.4471 - reconstruction_loss: 14377.0430 - vqvae_loss: 1712237.3317\n",
            "Epoch 935/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1697499.9688 - reconstruction_loss: 14347.3857 - vqvae_loss: 1683181.0721\n",
            "Epoch 936/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1708186.3293 - reconstruction_loss: 14318.8936 - vqvae_loss: 1693845.8053\n",
            "Epoch 937/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1732620.9183 - reconstruction_loss: 14332.8506 - vqvae_loss: 1718260.1178\n",
            "Epoch 938/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1739516.6490 - reconstruction_loss: 14351.3789 - vqvae_loss: 1725211.2572\n",
            "Epoch 939/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1769904.1731 - reconstruction_loss: 14361.2598 - vqvae_loss: 1755495.9495\n",
            "Epoch 940/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1795353.0745 - reconstruction_loss: 14431.0566 - vqvae_loss: 1780886.5769\n",
            "Epoch 941/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1773018.2861 - reconstruction_loss: 14335.4297 - vqvae_loss: 1758661.9111\n",
            "Epoch 942/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1757883.3702 - reconstruction_loss: 14337.3564 - vqvae_loss: 1743591.3990\n",
            "Epoch 943/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1766465.7620 - reconstruction_loss: 14348.8330 - vqvae_loss: 1752283.7548\n",
            "Epoch 944/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1818695.3293 - reconstruction_loss: 14349.3096 - vqvae_loss: 1804485.4519\n",
            "Epoch 945/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1826183.5913 - reconstruction_loss: 14335.8125 - vqvae_loss: 1811922.6082\n",
            "Epoch 946/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1783997.5312 - reconstruction_loss: 14299.3154 - vqvae_loss: 1769664.7981\n",
            "Epoch 947/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1765357.3197 - reconstruction_loss: 14294.4180 - vqvae_loss: 1751069.8606\n",
            "Epoch 948/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1764326.2476 - reconstruction_loss: 14270.6494 - vqvae_loss: 1750112.5240\n",
            "Epoch 949/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1758584.4543 - reconstruction_loss: 14278.8984 - vqvae_loss: 1744447.8606\n",
            "Epoch 950/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1728331.3582 - reconstruction_loss: 14298.4648 - vqvae_loss: 1713974.1538\n",
            "Epoch 951/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1736924.0000 - reconstruction_loss: 14285.3232 - vqvae_loss: 1722609.5889\n",
            "Epoch 952/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1733639.0385 - reconstruction_loss: 14309.4609 - vqvae_loss: 1719400.3317\n",
            "Epoch 953/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1734275.1899 - reconstruction_loss: 14353.9951 - vqvae_loss: 1719892.9303\n",
            "Epoch 954/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1729141.7043 - reconstruction_loss: 14326.3457 - vqvae_loss: 1714864.2909\n",
            "Epoch 955/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1752429.5913 - reconstruction_loss: 14364.2822 - vqvae_loss: 1738112.3486\n",
            "Epoch 956/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1737499.0986 - reconstruction_loss: 14326.5039 - vqvae_loss: 1723149.1755\n",
            "Epoch 957/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1740239.2043 - reconstruction_loss: 14293.9814 - vqvae_loss: 1725961.0457\n",
            "Epoch 958/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1731945.6178 - reconstruction_loss: 14302.0625 - vqvae_loss: 1717710.2139\n",
            "Epoch 959/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1758157.4856 - reconstruction_loss: 14253.7393 - vqvae_loss: 1743857.2885\n",
            "Epoch 960/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1767008.4880 - reconstruction_loss: 14252.5547 - vqvae_loss: 1752787.2716\n",
            "Epoch 961/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1783398.6058 - reconstruction_loss: 14253.0596 - vqvae_loss: 1769292.1010\n",
            "Epoch 962/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1745375.0913 - reconstruction_loss: 14217.2920 - vqvae_loss: 1731153.2067\n",
            "Epoch 963/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1737675.8053 - reconstruction_loss: 14217.1797 - vqvae_loss: 1723558.9688\n",
            "Epoch 964/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1755987.6010 - reconstruction_loss: 14204.7695 - vqvae_loss: 1741807.0601\n",
            "Epoch 965/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 1766204.0505 - reconstruction_loss: 14250.7168 - vqvae_loss: 1751811.6971\n",
            "Epoch 966/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1757974.3966 - reconstruction_loss: 14365.7490 - vqvae_loss: 1743582.8846\n",
            "Epoch 967/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1769677.5192 - reconstruction_loss: 14375.0859 - vqvae_loss: 1755309.5048\n",
            "Epoch 968/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1773457.5841 - reconstruction_loss: 14352.4033 - vqvae_loss: 1759159.7188\n",
            "Epoch 969/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1830957.5841 - reconstruction_loss: 14420.2939 - vqvae_loss: 1816484.4159\n",
            "Epoch 970/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1861364.0745 - reconstruction_loss: 14424.6670 - vqvae_loss: 1846992.7716\n",
            "Epoch 971/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1905294.7476 - reconstruction_loss: 14436.1885 - vqvae_loss: 1890937.2404\n",
            "Epoch 972/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1892102.3942 - reconstruction_loss: 14381.9727 - vqvae_loss: 1877783.8293\n",
            "Epoch 973/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1839472.7740 - reconstruction_loss: 14337.2412 - vqvae_loss: 1825150.4832\n",
            "Epoch 974/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1817526.7019 - reconstruction_loss: 14325.8799 - vqvae_loss: 1803223.4471\n",
            "Epoch 975/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1814202.6370 - reconstruction_loss: 14378.2070 - vqvae_loss: 1799901.3870\n",
            "Epoch 976/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1866679.8990 - reconstruction_loss: 14431.5820 - vqvae_loss: 1852215.0312\n",
            "Epoch 977/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1887272.0673 - reconstruction_loss: 14405.2637 - vqvae_loss: 1872941.2740\n",
            "Epoch 978/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1891919.8462 - reconstruction_loss: 14414.7832 - vqvae_loss: 1877617.4639\n",
            "Epoch 979/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1911615.6635 - reconstruction_loss: 14435.9453 - vqvae_loss: 1897081.4591\n",
            "Epoch 980/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1862936.8966 - reconstruction_loss: 14462.0488 - vqvae_loss: 1848565.9976\n",
            "Epoch 981/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1925257.0697 - reconstruction_loss: 14413.3555 - vqvae_loss: 1910959.8510\n",
            "Epoch 982/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1895888.1298 - reconstruction_loss: 14408.9072 - vqvae_loss: 1881577.5072\n",
            "Epoch 983/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1898781.9808 - reconstruction_loss: 14329.1729 - vqvae_loss: 1884385.0625\n",
            "Epoch 984/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1908560.1562 - reconstruction_loss: 14348.2441 - vqvae_loss: 1894281.6779\n",
            "Epoch 985/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1909596.9207 - reconstruction_loss: 14355.5527 - vqvae_loss: 1895241.8894\n",
            "Epoch 986/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1926617.5361 - reconstruction_loss: 14347.2686 - vqvae_loss: 1912152.1394\n",
            "Epoch 987/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1904534.6394 - reconstruction_loss: 14418.7490 - vqvae_loss: 1889967.6466\n",
            "Epoch 988/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1896696.6947 - reconstruction_loss: 14344.4219 - vqvae_loss: 1882309.7572\n",
            "Epoch 989/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1936596.1514 - reconstruction_loss: 14416.7393 - vqvae_loss: 1922292.5889\n",
            "Epoch 990/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1962966.0192 - reconstruction_loss: 14432.6504 - vqvae_loss: 1948518.8918\n",
            "Epoch 991/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1953028.8438 - reconstruction_loss: 14384.2178 - vqvae_loss: 1938595.6683\n",
            "Epoch 992/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1937331.0505 - reconstruction_loss: 14334.5996 - vqvae_loss: 1923020.5409\n",
            "Epoch 993/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1958022.8870 - reconstruction_loss: 14340.0088 - vqvae_loss: 1943688.9615\n",
            "Epoch 994/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1953390.6707 - reconstruction_loss: 14500.9297 - vqvae_loss: 1938985.0192\n",
            "Epoch 995/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1963984.1707 - reconstruction_loss: 14406.8828 - vqvae_loss: 1949666.0962\n",
            "Epoch 996/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1955498.0264 - reconstruction_loss: 14338.3750 - vqvae_loss: 1941115.5264\n",
            "Epoch 997/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1959994.8558 - reconstruction_loss: 14382.2822 - vqvae_loss: 1945673.5048\n",
            "Epoch 998/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1963061.3438 - reconstruction_loss: 14307.6240 - vqvae_loss: 1948690.6659\n",
            "Epoch 999/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1978975.0216 - reconstruction_loss: 14408.4805 - vqvae_loss: 1964669.7139\n",
            "Epoch 1000/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1978631.9087 - reconstruction_loss: 14339.4551 - vqvae_loss: 1964321.2332\n",
            "Epoch 1001/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1993235.0240 - reconstruction_loss: 14281.4668 - vqvae_loss: 1978954.2500\n",
            "Epoch 1002/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2013111.2957 - reconstruction_loss: 14340.5713 - vqvae_loss: 1998896.2043\n",
            "Epoch 1003/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1999703.3197 - reconstruction_loss: 14385.7021 - vqvae_loss: 1985349.8942\n",
            "Epoch 1004/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2017040.2644 - reconstruction_loss: 14269.4199 - vqvae_loss: 2002923.8606\n",
            "Epoch 1005/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 2007597.8510 - reconstruction_loss: 14226.8936 - vqvae_loss: 1993289.9736\n",
            "Epoch 1006/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1997727.2837 - reconstruction_loss: 14236.9551 - vqvae_loss: 1983421.6514\n",
            "Epoch 1007/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2011902.6058 - reconstruction_loss: 14226.6504 - vqvae_loss: 1997661.5986\n",
            "Epoch 1008/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 2014671.4062 - reconstruction_loss: 14298.2842 - vqvae_loss: 2000368.3197\n",
            "Epoch 1009/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2031642.0817 - reconstruction_loss: 14276.8691 - vqvae_loss: 2017303.5529\n",
            "Epoch 1010/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2014871.6010 - reconstruction_loss: 14263.8223 - vqvae_loss: 2000638.7019\n",
            "Epoch 1011/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2024545.2524 - reconstruction_loss: 14282.3955 - vqvae_loss: 2010264.6106\n",
            "Epoch 1012/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2015043.9904 - reconstruction_loss: 14237.9678 - vqvae_loss: 2000719.5865\n",
            "Epoch 1013/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 2011861.5144 - reconstruction_loss: 14246.7656 - vqvae_loss: 1997672.0769\n",
            "Epoch 1014/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2030701.7043 - reconstruction_loss: 14259.3643 - vqvae_loss: 2016414.8966\n",
            "Epoch 1015/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2024356.6250 - reconstruction_loss: 14222.1680 - vqvae_loss: 2010157.4038\n",
            "Epoch 1016/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2051156.9495 - reconstruction_loss: 14260.5342 - vqvae_loss: 2036838.3870\n",
            "Epoch 1017/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 2036062.5697 - reconstruction_loss: 14177.5967 - vqvae_loss: 2021883.8438\n",
            "Epoch 1018/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2033821.3510 - reconstruction_loss: 14190.3262 - vqvae_loss: 2019579.4591\n",
            "Epoch 1019/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2012799.2308 - reconstruction_loss: 14183.1436 - vqvae_loss: 1998668.1418\n",
            "Epoch 1020/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2023005.5553 - reconstruction_loss: 14155.1455 - vqvae_loss: 2008859.1514\n",
            "Epoch 1021/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2037317.4087 - reconstruction_loss: 14139.8086 - vqvae_loss: 2023034.0096\n",
            "Epoch 1022/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2001210.9928 - reconstruction_loss: 14126.4814 - vqvae_loss: 1987133.1394\n",
            "Epoch 1023/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2020778.3438 - reconstruction_loss: 14118.0771 - vqvae_loss: 2006715.3678\n",
            "Epoch 1024/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 2012871.2548 - reconstruction_loss: 14060.5830 - vqvae_loss: 1998791.6130\n",
            "Epoch 1025/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 2005532.0072 - reconstruction_loss: 14058.3467 - vqvae_loss: 1991479.3149\n",
            "Epoch 1026/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 2019574.1226 - reconstruction_loss: 14034.4795 - vqvae_loss: 2005425.0216\n",
            "Epoch 1027/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1997971.6298 - reconstruction_loss: 14038.4551 - vqvae_loss: 1983866.9495\n",
            "Epoch 1028/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 2016975.8077 - reconstruction_loss: 14018.0879 - vqvae_loss: 2002845.2716\n",
            "Epoch 1029/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1987409.9231 - reconstruction_loss: 14065.0225 - vqvae_loss: 1973335.4712\n",
            "Epoch 1030/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1971822.5793 - reconstruction_loss: 13953.8320 - vqvae_loss: 1957754.1322\n",
            "Epoch 1031/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1974002.2043 - reconstruction_loss: 13967.7217 - vqvae_loss: 1960083.5361\n",
            "Epoch 1032/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1963507.3534 - reconstruction_loss: 13930.0332 - vqvae_loss: 1949588.8534\n",
            "Epoch 1033/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1967519.7115 - reconstruction_loss: 13914.8447 - vqvae_loss: 1953602.5817\n",
            "Epoch 1034/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1961979.6058 - reconstruction_loss: 13911.2217 - vqvae_loss: 1948219.9736\n",
            "Epoch 1035/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1938362.8125 - reconstruction_loss: 13884.4697 - vqvae_loss: 1924510.2909\n",
            "Epoch 1036/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1936386.5433 - reconstruction_loss: 13846.7656 - vqvae_loss: 1922415.6322\n",
            "Epoch 1037/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1933589.9688 - reconstruction_loss: 13893.6250 - vqvae_loss: 1919783.1130\n",
            "Epoch 1038/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1928401.5216 - reconstruction_loss: 13882.4297 - vqvae_loss: 1914486.1274\n",
            "Epoch 1039/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1947062.8966 - reconstruction_loss: 13869.8223 - vqvae_loss: 1933269.1082\n",
            "Epoch 1040/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1952165.5529 - reconstruction_loss: 13865.8809 - vqvae_loss: 1938295.2524\n",
            "Epoch 1041/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1928771.7885 - reconstruction_loss: 13873.4961 - vqvae_loss: 1914932.9207\n",
            "Epoch 1042/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1934413.0986 - reconstruction_loss: 13850.6387 - vqvae_loss: 1920630.3942\n",
            "Epoch 1043/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1946815.8510 - reconstruction_loss: 13923.7402 - vqvae_loss: 1932906.3029\n",
            "Epoch 1044/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1975727.9279 - reconstruction_loss: 14027.3418 - vqvae_loss: 1961696.6707\n",
            "Epoch 1045/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1954351.0240 - reconstruction_loss: 14003.7773 - vqvae_loss: 1940488.7115\n",
            "Epoch 1046/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1962207.8894 - reconstruction_loss: 13952.7314 - vqvae_loss: 1948166.4279\n",
            "Epoch 1047/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1933094.8341 - reconstruction_loss: 13838.4453 - vqvae_loss: 1919165.3966\n",
            "Epoch 1048/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1927860.5337 - reconstruction_loss: 13817.2803 - vqvae_loss: 1914019.5745\n",
            "Epoch 1049/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1914292.6490 - reconstruction_loss: 13813.1260 - vqvae_loss: 1900634.3053\n",
            "Epoch 1050/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1918259.0240 - reconstruction_loss: 13798.4043 - vqvae_loss: 1904593.0264\n",
            "Epoch 1051/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1937161.7620 - reconstruction_loss: 13887.7158 - vqvae_loss: 1923367.2404\n",
            "Epoch 1052/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1922202.9303 - reconstruction_loss: 13770.1152 - vqvae_loss: 1908335.3486\n",
            "Epoch 1053/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1900775.2091 - reconstruction_loss: 13742.3086 - vqvae_loss: 1887035.5505\n",
            "Epoch 1054/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1900797.7909 - reconstruction_loss: 13793.1006 - vqvae_loss: 1887065.8558\n",
            "Epoch 1055/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1894070.8510 - reconstruction_loss: 13774.1445 - vqvae_loss: 1880254.2596\n",
            "Epoch 1056/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1900296.3510 - reconstruction_loss: 13780.1299 - vqvae_loss: 1886496.7067\n",
            "Epoch 1057/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1864354.5192 - reconstruction_loss: 13781.1406 - vqvae_loss: 1850574.9519\n",
            "Epoch 1058/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1873303.0385 - reconstruction_loss: 13764.5801 - vqvae_loss: 1859578.2188\n",
            "Epoch 1059/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1867272.8582 - reconstruction_loss: 13781.2227 - vqvae_loss: 1853445.5048\n",
            "Epoch 1060/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 1852678.9038 - reconstruction_loss: 13760.0957 - vqvae_loss: 1838927.1346\n",
            "Epoch 1061/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1838865.1322 - reconstruction_loss: 13763.3213 - vqvae_loss: 1825025.7764\n",
            "Epoch 1062/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1832921.9663 - reconstruction_loss: 13765.7881 - vqvae_loss: 1819115.2861\n",
            "Epoch 1063/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1820109.1106 - reconstruction_loss: 13746.5176 - vqvae_loss: 1806451.1995\n",
            "Epoch 1064/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1842749.4351 - reconstruction_loss: 13761.8164 - vqvae_loss: 1829031.2139\n",
            "Epoch 1065/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1848675.3438 - reconstruction_loss: 13741.9658 - vqvae_loss: 1834970.0962\n",
            "Epoch 1066/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1881010.4087 - reconstruction_loss: 13857.5049 - vqvae_loss: 1867218.7163\n",
            "Epoch 1067/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1874334.1851 - reconstruction_loss: 13986.2207 - vqvae_loss: 1860380.9543\n",
            "Epoch 1068/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1907888.9207 - reconstruction_loss: 14061.0684 - vqvae_loss: 1893862.8125\n",
            "Epoch 1069/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1894739.0240 - reconstruction_loss: 13982.2627 - vqvae_loss: 1880822.2524\n",
            "Epoch 1070/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1875220.1683 - reconstruction_loss: 14005.4023 - vqvae_loss: 1861314.6130\n",
            "Epoch 1071/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1881955.2091 - reconstruction_loss: 13894.7725 - vqvae_loss: 1868116.7548\n",
            "Epoch 1072/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1859640.6106 - reconstruction_loss: 13885.6504 - vqvae_loss: 1845798.8942\n",
            "Epoch 1073/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1856147.8125 - reconstruction_loss: 13913.8135 - vqvae_loss: 1842329.6635\n",
            "Epoch 1074/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1841440.3365 - reconstruction_loss: 13895.3574 - vqvae_loss: 1827557.0264\n",
            "Epoch 1075/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1819378.9087 - reconstruction_loss: 13971.5908 - vqvae_loss: 1805384.3894\n",
            "Epoch 1076/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1832307.5433 - reconstruction_loss: 13980.3252 - vqvae_loss: 1818208.2284\n",
            "Epoch 1077/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1801768.0769 - reconstruction_loss: 13882.6641 - vqvae_loss: 1787877.0072\n",
            "Epoch 1078/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1803645.5889 - reconstruction_loss: 13941.9463 - vqvae_loss: 1789664.5240\n",
            "Epoch 1079/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1832920.1466 - reconstruction_loss: 14066.1348 - vqvae_loss: 1818919.3438\n",
            "Epoch 1080/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1800175.1178 - reconstruction_loss: 14008.6572 - vqvae_loss: 1786195.9784\n",
            "Epoch 1081/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1788805.3510 - reconstruction_loss: 13971.3203 - vqvae_loss: 1774752.0048\n",
            "Epoch 1082/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1767442.1827 - reconstruction_loss: 13974.3799 - vqvae_loss: 1753449.1538\n",
            "Epoch 1083/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1760384.2668 - reconstruction_loss: 13965.3496 - vqvae_loss: 1746572.3750\n",
            "Epoch 1084/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1759427.6106 - reconstruction_loss: 13992.2871 - vqvae_loss: 1745305.0481\n",
            "Epoch 1085/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1739042.4471 - reconstruction_loss: 14062.2842 - vqvae_loss: 1725068.2572\n",
            "Epoch 1086/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1749810.5312 - reconstruction_loss: 13981.3691 - vqvae_loss: 1735810.6538\n",
            "Epoch 1087/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1765387.0529 - reconstruction_loss: 14012.2002 - vqvae_loss: 1751288.7861\n",
            "Epoch 1088/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1726698.0721 - reconstruction_loss: 13993.4717 - vqvae_loss: 1712696.2933\n",
            "Epoch 1089/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1718386.7909 - reconstruction_loss: 13993.0029 - vqvae_loss: 1704419.8606\n",
            "Epoch 1090/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1708620.8245 - reconstruction_loss: 14050.7354 - vqvae_loss: 1694626.0601\n",
            "Epoch 1091/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1719007.2933 - reconstruction_loss: 14040.3213 - vqvae_loss: 1704958.1635\n",
            "Epoch 1092/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1708616.8774 - reconstruction_loss: 14027.7402 - vqvae_loss: 1694597.0625\n",
            "Epoch 1093/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1706340.6875 - reconstruction_loss: 13984.4707 - vqvae_loss: 1692345.0865\n",
            "Epoch 1094/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1686127.1418 - reconstruction_loss: 14082.3779 - vqvae_loss: 1672110.9375\n",
            "Epoch 1095/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1672605.1058 - reconstruction_loss: 14008.0430 - vqvae_loss: 1658712.8125\n",
            "Epoch 1096/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1672419.1635 - reconstruction_loss: 14017.2451 - vqvae_loss: 1658565.0337\n",
            "Epoch 1097/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1664410.3966 - reconstruction_loss: 13973.1787 - vqvae_loss: 1650528.3317\n",
            "Epoch 1098/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1654146.7284 - reconstruction_loss: 13929.2988 - vqvae_loss: 1640275.3077\n",
            "Epoch 1099/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1621767.8822 - reconstruction_loss: 13963.6338 - vqvae_loss: 1607895.9832\n",
            "Epoch 1100/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1620867.9736 - reconstruction_loss: 13963.3232 - vqvae_loss: 1606832.4087\n",
            "Epoch 1101/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1595809.2837 - reconstruction_loss: 13935.4326 - vqvae_loss: 1581788.7236\n",
            "Epoch 1102/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1595980.7596 - reconstruction_loss: 13941.6865 - vqvae_loss: 1581949.8341\n",
            "Epoch 1103/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1596787.4567 - reconstruction_loss: 13904.5918 - vqvae_loss: 1582803.2620\n",
            "Epoch 1104/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1581533.7332 - reconstruction_loss: 13997.6436 - vqvae_loss: 1567536.9808\n",
            "Epoch 1105/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1577268.6635 - reconstruction_loss: 13890.6172 - vqvae_loss: 1563455.1514\n",
            "Epoch 1106/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1557028.6130 - reconstruction_loss: 13850.8906 - vqvae_loss: 1543189.3678\n",
            "Epoch 1107/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1545873.4255 - reconstruction_loss: 13873.1523 - vqvae_loss: 1531839.9639\n",
            "Epoch 1108/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 1526323.6130 - reconstruction_loss: 13937.2207 - vqvae_loss: 1512282.1611\n",
            "Epoch 1109/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1531088.0144 - reconstruction_loss: 13811.1338 - vqvae_loss: 1517281.8413\n",
            "Epoch 1110/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1504709.5216 - reconstruction_loss: 13882.8945 - vqvae_loss: 1490809.0553\n",
            "Epoch 1111/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1500806.0889 - reconstruction_loss: 13813.9346 - vqvae_loss: 1486980.2716\n",
            "Epoch 1112/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1483583.1899 - reconstruction_loss: 13808.7275 - vqvae_loss: 1469738.3173\n",
            "Epoch 1113/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1472342.4928 - reconstruction_loss: 13801.1631 - vqvae_loss: 1458581.0865\n",
            "Epoch 1114/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1460963.5457 - reconstruction_loss: 13774.6436 - vqvae_loss: 1447222.8221\n",
            "Epoch 1115/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1443166.3365 - reconstruction_loss: 13736.6953 - vqvae_loss: 1429443.9832\n",
            "Epoch 1116/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1432828.7019 - reconstruction_loss: 13736.5918 - vqvae_loss: 1419097.5096\n",
            "Epoch 1117/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1424166.9832 - reconstruction_loss: 13719.9961 - vqvae_loss: 1410518.1418\n",
            "Epoch 1118/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1419524.2428 - reconstruction_loss: 13708.8477 - vqvae_loss: 1405866.9375\n",
            "Epoch 1119/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1416796.2957 - reconstruction_loss: 13710.6016 - vqvae_loss: 1402995.3462\n",
            "Epoch 1120/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1397295.5433 - reconstruction_loss: 13723.9775 - vqvae_loss: 1383578.7332\n",
            "Epoch 1121/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1401544.9543 - reconstruction_loss: 13657.4346 - vqvae_loss: 1387908.1635\n",
            "Epoch 1122/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1389048.0601 - reconstruction_loss: 13651.0420 - vqvae_loss: 1375295.2572\n",
            "Epoch 1123/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1375281.0096 - reconstruction_loss: 13710.0713 - vqvae_loss: 1361481.5938\n",
            "Epoch 1124/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1370681.4856 - reconstruction_loss: 13739.8018 - vqvae_loss: 1356956.4327\n",
            "Epoch 1125/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1349990.4543 - reconstruction_loss: 13693.0801 - vqvae_loss: 1336317.9832\n",
            "Epoch 1126/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1357034.4808 - reconstruction_loss: 13688.1504 - vqvae_loss: 1343285.5048\n",
            "Epoch 1127/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1341325.0288 - reconstruction_loss: 13648.3926 - vqvae_loss: 1327730.5697\n",
            "Epoch 1128/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1342606.8365 - reconstruction_loss: 13666.4795 - vqvae_loss: 1329025.0216\n",
            "Epoch 1129/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1328674.3630 - reconstruction_loss: 13619.4102 - vqvae_loss: 1314986.1106\n",
            "Epoch 1130/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1317069.5385 - reconstruction_loss: 13607.0049 - vqvae_loss: 1303437.3221\n",
            "Epoch 1131/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1313331.3654 - reconstruction_loss: 13586.3018 - vqvae_loss: 1299787.1875\n",
            "Epoch 1132/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1304239.1250 - reconstruction_loss: 13578.2461 - vqvae_loss: 1290603.4038\n",
            "Epoch 1133/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1299183.7548 - reconstruction_loss: 13582.3320 - vqvae_loss: 1285603.9519\n",
            "Epoch 1134/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1305434.4543 - reconstruction_loss: 13593.1309 - vqvae_loss: 1291850.1106\n",
            "Epoch 1135/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1304912.7308 - reconstruction_loss: 13553.2588 - vqvae_loss: 1291313.3101\n",
            "Epoch 1136/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1300730.7957 - reconstruction_loss: 13570.0859 - vqvae_loss: 1287158.0889\n",
            "Epoch 1137/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1288108.0889 - reconstruction_loss: 13569.4551 - vqvae_loss: 1274626.4038\n",
            "Epoch 1138/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1294617.4423 - reconstruction_loss: 13527.1631 - vqvae_loss: 1281041.1130\n",
            "Epoch 1139/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1281194.0072 - reconstruction_loss: 13518.3779 - vqvae_loss: 1267651.6803\n",
            "Epoch 1140/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1276066.5433 - reconstruction_loss: 13498.4951 - vqvae_loss: 1262601.8630\n",
            "Epoch 1141/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1273622.2500 - reconstruction_loss: 13499.2822 - vqvae_loss: 1260110.0264\n",
            "Epoch 1142/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1272169.7716 - reconstruction_loss: 13529.4629 - vqvae_loss: 1258778.4808\n",
            "Epoch 1143/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1283871.1755 - reconstruction_loss: 13494.7793 - vqvae_loss: 1270324.5024\n",
            "Epoch 1144/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1261239.3053 - reconstruction_loss: 13489.7393 - vqvae_loss: 1247716.9567\n",
            "Epoch 1145/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1267506.9952 - reconstruction_loss: 13479.4580 - vqvae_loss: 1253847.7139\n",
            "Epoch 1146/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1257433.9471 - reconstruction_loss: 13474.5684 - vqvae_loss: 1244069.6995\n",
            "Epoch 1147/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1264926.3413 - reconstruction_loss: 13456.4863 - vqvae_loss: 1251528.6250\n",
            "Epoch 1148/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1275609.8005 - reconstruction_loss: 13465.7939 - vqvae_loss: 1262180.4615\n",
            "Epoch 1149/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1273910.0024 - reconstruction_loss: 13455.0146 - vqvae_loss: 1260382.8606\n",
            "Epoch 1150/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1282473.0361 - reconstruction_loss: 13499.4229 - vqvae_loss: 1268947.7933\n",
            "Epoch 1151/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1274899.6274 - reconstruction_loss: 13485.7246 - vqvae_loss: 1261492.7596\n",
            "Epoch 1152/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1275929.4303 - reconstruction_loss: 13468.0127 - vqvae_loss: 1262426.0240\n",
            "Epoch 1153/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1282794.1803 - reconstruction_loss: 13469.9863 - vqvae_loss: 1269412.5457\n",
            "Epoch 1154/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1298795.9303 - reconstruction_loss: 13491.7168 - vqvae_loss: 1285337.1274\n",
            "Epoch 1155/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 1305420.5769 - reconstruction_loss: 13491.1045 - vqvae_loss: 1291887.7596\n",
            "Epoch 1156/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1290588.8101 - reconstruction_loss: 13466.5547 - vqvae_loss: 1277159.6875\n",
            "Epoch 1157/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1295894.2788 - reconstruction_loss: 13510.4326 - vqvae_loss: 1282496.6394\n",
            "Epoch 1158/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1302333.4543 - reconstruction_loss: 13483.8330 - vqvae_loss: 1288803.8245\n",
            "Epoch 1159/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1300497.2380 - reconstruction_loss: 13489.3877 - vqvae_loss: 1287017.4495\n",
            "Epoch 1160/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1341455.1322 - reconstruction_loss: 13487.1104 - vqvae_loss: 1327815.9784\n",
            "Epoch 1161/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1316394.7428 - reconstruction_loss: 13533.4346 - vqvae_loss: 1302813.5240\n",
            "Epoch 1162/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1336431.4423 - reconstruction_loss: 13525.8809 - vqvae_loss: 1323002.9712\n",
            "Epoch 1163/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1361566.0553 - reconstruction_loss: 13608.9551 - vqvae_loss: 1348049.4712\n",
            "Epoch 1164/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1362890.8654 - reconstruction_loss: 13562.0635 - vqvae_loss: 1349303.6130\n",
            "Epoch 1165/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1375629.2260 - reconstruction_loss: 13546.6582 - vqvae_loss: 1362039.8293\n",
            "Epoch 1166/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1392892.9423 - reconstruction_loss: 13543.7217 - vqvae_loss: 1379327.1875\n",
            "Epoch 1167/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1372994.0481 - reconstruction_loss: 13529.2002 - vqvae_loss: 1359446.3582\n",
            "Epoch 1168/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1383411.7380 - reconstruction_loss: 13577.6436 - vqvae_loss: 1369898.1082\n",
            "Epoch 1169/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1407142.0337 - reconstruction_loss: 13544.7109 - vqvae_loss: 1393603.4543\n",
            "Epoch 1170/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1410037.7019 - reconstruction_loss: 13558.4453 - vqvae_loss: 1396537.2716\n",
            "Epoch 1171/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1413018.1707 - reconstruction_loss: 13539.7793 - vqvae_loss: 1399440.5529\n",
            "Epoch 1172/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1425046.7452 - reconstruction_loss: 13572.6357 - vqvae_loss: 1411568.1827\n",
            "Epoch 1173/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1435986.4543 - reconstruction_loss: 13620.5654 - vqvae_loss: 1422462.1202\n",
            "Epoch 1174/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1436888.9447 - reconstruction_loss: 13596.6787 - vqvae_loss: 1423243.3341\n",
            "Epoch 1175/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1438026.8774 - reconstruction_loss: 13579.8799 - vqvae_loss: 1424316.1370\n",
            "Epoch 1176/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1441525.9255 - reconstruction_loss: 13566.5820 - vqvae_loss: 1427849.0457\n",
            "Epoch 1177/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1440606.2620 - reconstruction_loss: 13571.1250 - vqvae_loss: 1427008.3702\n",
            "Epoch 1178/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1461777.5625 - reconstruction_loss: 13560.0752 - vqvae_loss: 1448076.3365\n",
            "Epoch 1179/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1467107.5697 - reconstruction_loss: 13558.5039 - vqvae_loss: 1453450.8438\n",
            "Epoch 1180/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1465068.5168 - reconstruction_loss: 13568.0352 - vqvae_loss: 1451482.9255\n",
            "Epoch 1181/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1468363.2356 - reconstruction_loss: 13576.6523 - vqvae_loss: 1454823.9663\n",
            "Epoch 1182/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1494563.1587 - reconstruction_loss: 13621.3379 - vqvae_loss: 1480850.2740\n",
            "Epoch 1183/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1503894.4014 - reconstruction_loss: 13564.1045 - vqvae_loss: 1490309.7404\n",
            "Epoch 1184/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1495186.1346 - reconstruction_loss: 13611.7197 - vqvae_loss: 1481528.5913\n",
            "Epoch 1185/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1493164.4111 - reconstruction_loss: 13603.9189 - vqvae_loss: 1479640.0264\n",
            "Epoch 1186/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1507122.5337 - reconstruction_loss: 13590.5527 - vqvae_loss: 1493510.2957\n",
            "Epoch 1187/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1508309.7644 - reconstruction_loss: 13633.7344 - vqvae_loss: 1494778.2115\n",
            "Epoch 1188/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1508514.9014 - reconstruction_loss: 13570.9756 - vqvae_loss: 1494906.2212\n",
            "Epoch 1189/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1494412.3029 - reconstruction_loss: 13645.3018 - vqvae_loss: 1480827.2091\n",
            "Epoch 1190/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1534535.6947 - reconstruction_loss: 13641.6582 - vqvae_loss: 1520957.3365\n",
            "Epoch 1191/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1528235.3846 - reconstruction_loss: 13638.3154 - vqvae_loss: 1514556.2404\n",
            "Epoch 1192/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1520052.8053 - reconstruction_loss: 13613.4365 - vqvae_loss: 1506462.2308\n",
            "Epoch 1193/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1523017.6178 - reconstruction_loss: 13589.5527 - vqvae_loss: 1509420.4303\n",
            "Epoch 1194/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1495038.7596 - reconstruction_loss: 13568.4521 - vqvae_loss: 1481541.0913\n",
            "Epoch 1195/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1511509.0865 - reconstruction_loss: 13587.0635 - vqvae_loss: 1497993.3606\n",
            "Epoch 1196/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1484498.6659 - reconstruction_loss: 13614.4082 - vqvae_loss: 1470983.6490\n",
            "Epoch 1197/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1505750.2139 - reconstruction_loss: 13645.6689 - vqvae_loss: 1492144.4207\n",
            "Epoch 1198/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1519681.4399 - reconstruction_loss: 13678.2256 - vqvae_loss: 1505963.9087\n",
            "Epoch 1199/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1486471.1034 - reconstruction_loss: 13631.3691 - vqvae_loss: 1472893.9111\n",
            "Epoch 1200/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1501811.7452 - reconstruction_loss: 13696.0430 - vqvae_loss: 1488037.7909\n",
            "Epoch 1201/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1515520.7428 - reconstruction_loss: 13759.2266 - vqvae_loss: 1501770.4736\n",
            "Epoch 1202/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1521791.7548 - reconstruction_loss: 13728.2754 - vqvae_loss: 1508038.4543\n",
            "Epoch 1203/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1475598.0385 - reconstruction_loss: 13733.9727 - vqvae_loss: 1461790.0096\n",
            "Epoch 1204/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1545673.2452 - reconstruction_loss: 13701.2842 - vqvae_loss: 1531892.3197\n",
            "Epoch 1205/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1525209.4567 - reconstruction_loss: 13692.2988 - vqvae_loss: 1511434.5096\n",
            "Epoch 1206/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1497383.0697 - reconstruction_loss: 13624.9316 - vqvae_loss: 1483729.1442\n",
            "Epoch 1207/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1457565.9784 - reconstruction_loss: 13626.6260 - vqvae_loss: 1443865.5817\n",
            "Epoch 1208/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1459975.3822 - reconstruction_loss: 13595.1621 - vqvae_loss: 1446435.3245\n",
            "Epoch 1209/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1475958.3534 - reconstruction_loss: 13602.7588 - vqvae_loss: 1462466.4952\n",
            "Epoch 1210/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1478940.0625 - reconstruction_loss: 13636.2451 - vqvae_loss: 1465361.8870\n",
            "Epoch 1211/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1443095.3846 - reconstruction_loss: 13602.4053 - vqvae_loss: 1429454.0481\n",
            "Epoch 1212/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1462315.8221 - reconstruction_loss: 13619.7891 - vqvae_loss: 1448683.0312\n",
            "Epoch 1213/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1470279.9591 - reconstruction_loss: 13564.0801 - vqvae_loss: 1456784.4615\n",
            "Epoch 1214/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1455003.2812 - reconstruction_loss: 13554.5186 - vqvae_loss: 1441511.9688\n",
            "Epoch 1215/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1434759.7668 - reconstruction_loss: 13505.8174 - vqvae_loss: 1421200.2356\n",
            "Epoch 1216/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1445661.7428 - reconstruction_loss: 13452.7656 - vqvae_loss: 1432095.2091\n",
            "Epoch 1217/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1447817.2764 - reconstruction_loss: 13517.6143 - vqvae_loss: 1434220.7284\n",
            "Epoch 1218/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1420058.1659 - reconstruction_loss: 13553.1875 - vqvae_loss: 1406517.3077\n",
            "Epoch 1219/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1430452.7139 - reconstruction_loss: 13536.9326 - vqvae_loss: 1416931.9928\n",
            "Epoch 1220/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1447958.2524 - reconstruction_loss: 13590.5859 - vqvae_loss: 1434318.9832\n",
            "Epoch 1221/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1461595.5649 - reconstruction_loss: 13541.2461 - vqvae_loss: 1447966.1659\n",
            "Epoch 1222/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1409695.0745 - reconstruction_loss: 13414.1113 - vqvae_loss: 1396355.5817\n",
            "Epoch 1223/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1441445.8438 - reconstruction_loss: 13467.3066 - vqvae_loss: 1427863.7524\n",
            "Epoch 1224/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1446331.3510 - reconstruction_loss: 13542.8643 - vqvae_loss: 1432772.2812\n",
            "Epoch 1225/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1430552.2091 - reconstruction_loss: 13460.6426 - vqvae_loss: 1417059.6298\n",
            "Epoch 1226/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1405665.3053 - reconstruction_loss: 13482.0195 - vqvae_loss: 1392159.4952\n",
            "Epoch 1227/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1426398.5337 - reconstruction_loss: 13472.7441 - vqvae_loss: 1413031.3101\n",
            "Epoch 1228/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1435191.0024 - reconstruction_loss: 13533.1436 - vqvae_loss: 1421505.0144\n",
            "Epoch 1229/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1413641.9423 - reconstruction_loss: 13470.9561 - vqvae_loss: 1400257.9928\n",
            "Epoch 1230/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1409586.1875 - reconstruction_loss: 13469.6387 - vqvae_loss: 1396013.9567\n",
            "Epoch 1231/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1398211.8726 - reconstruction_loss: 13435.9600 - vqvae_loss: 1384656.1683\n",
            "Epoch 1232/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1399755.9952 - reconstruction_loss: 13415.0010 - vqvae_loss: 1386458.3053\n",
            "Epoch 1233/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1392841.4471 - reconstruction_loss: 13353.5586 - vqvae_loss: 1379414.6875\n",
            "Epoch 1234/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1370373.9231 - reconstruction_loss: 13462.2363 - vqvae_loss: 1356910.0192\n",
            "Epoch 1235/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1381058.8822 - reconstruction_loss: 13356.7207 - vqvae_loss: 1367769.2957\n",
            "Epoch 1236/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1392923.9543 - reconstruction_loss: 13342.5820 - vqvae_loss: 1379684.6562\n",
            "Epoch 1237/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1372032.6875 - reconstruction_loss: 13296.6621 - vqvae_loss: 1358747.0841\n",
            "Epoch 1238/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1352118.3221 - reconstruction_loss: 13280.7891 - vqvae_loss: 1338769.6370\n",
            "Epoch 1239/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1356458.2981 - reconstruction_loss: 13284.3037 - vqvae_loss: 1343212.0192\n",
            "Epoch 1240/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1362725.9519 - reconstruction_loss: 13286.4971 - vqvae_loss: 1349356.8413\n",
            "Epoch 1241/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1350306.2909 - reconstruction_loss: 13375.9180 - vqvae_loss: 1336959.0817\n",
            "Epoch 1242/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1350241.6418 - reconstruction_loss: 13350.8887 - vqvae_loss: 1336966.1130\n",
            "Epoch 1243/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1345312.1274 - reconstruction_loss: 13344.1309 - vqvae_loss: 1331967.8582\n",
            "Epoch 1244/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1350303.4111 - reconstruction_loss: 13334.4707 - vqvae_loss: 1336974.8942\n",
            "Epoch 1245/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1357790.0601 - reconstruction_loss: 13348.8135 - vqvae_loss: 1344478.8750\n",
            "Epoch 1246/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1337272.0697 - reconstruction_loss: 13384.5850 - vqvae_loss: 1323957.9688\n",
            "Epoch 1247/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1319494.8125 - reconstruction_loss: 13367.3838 - vqvae_loss: 1306159.3966\n",
            "Epoch 1248/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1326922.9928 - reconstruction_loss: 13312.0127 - vqvae_loss: 1313625.5817\n",
            "Epoch 1249/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1326184.2163 - reconstruction_loss: 13270.6523 - vqvae_loss: 1312904.3798\n",
            "Epoch 1250/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 1316387.1538 - reconstruction_loss: 13280.7930 - vqvae_loss: 1303175.8462\n",
            "Epoch 1251/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1304217.9688 - reconstruction_loss: 13312.5732 - vqvae_loss: 1290875.1226\n",
            "Epoch 1252/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1324592.1731 - reconstruction_loss: 13335.0537 - vqvae_loss: 1311219.4303\n",
            "Epoch 1253/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1286493.4207 - reconstruction_loss: 13292.2852 - vqvae_loss: 1273170.7812\n",
            "Epoch 1254/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1310408.5673 - reconstruction_loss: 13385.8945 - vqvae_loss: 1297093.3486\n",
            "Epoch 1255/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1292711.0529 - reconstruction_loss: 13371.5566 - vqvae_loss: 1279270.5769\n",
            "Epoch 1256/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1278860.5817 - reconstruction_loss: 13358.1309 - vqvae_loss: 1265607.0938\n",
            "Epoch 1257/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1274739.4159 - reconstruction_loss: 13314.7305 - vqvae_loss: 1261397.1947\n",
            "Epoch 1258/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1289415.9038 - reconstruction_loss: 13313.5371 - vqvae_loss: 1276132.3774\n",
            "Epoch 1259/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1283056.6394 - reconstruction_loss: 13304.6787 - vqvae_loss: 1269720.3486\n",
            "Epoch 1260/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1267273.0986 - reconstruction_loss: 13318.4033 - vqvae_loss: 1254005.3534\n",
            "Epoch 1261/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1264848.5096 - reconstruction_loss: 13282.1904 - vqvae_loss: 1251402.5385\n",
            "Epoch 1262/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1265089.0889 - reconstruction_loss: 13333.9033 - vqvae_loss: 1251733.8870\n",
            "Epoch 1263/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1260560.8750 - reconstruction_loss: 13300.7598 - vqvae_loss: 1247279.5048\n",
            "Epoch 1264/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1253001.0649 - reconstruction_loss: 13417.1699 - vqvae_loss: 1239719.1490\n",
            "Epoch 1265/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1272858.2596 - reconstruction_loss: 13335.9521 - vqvae_loss: 1259401.8053\n",
            "Epoch 1266/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1255726.0481 - reconstruction_loss: 13360.6289 - vqvae_loss: 1242414.5048\n",
            "Epoch 1267/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1247986.7212 - reconstruction_loss: 13319.2783 - vqvae_loss: 1234618.0673\n",
            "Epoch 1268/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1268138.4447 - reconstruction_loss: 13394.0781 - vqvae_loss: 1254756.4519\n",
            "Epoch 1269/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1285220.1370 - reconstruction_loss: 13389.1572 - vqvae_loss: 1271791.6587\n",
            "Epoch 1270/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1250457.5817 - reconstruction_loss: 13434.1904 - vqvae_loss: 1237015.8341\n",
            "Epoch 1271/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1254440.1370 - reconstruction_loss: 13380.0176 - vqvae_loss: 1241028.2067\n",
            "Epoch 1272/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1276437.5433 - reconstruction_loss: 13461.6953 - vqvae_loss: 1262945.6466\n",
            "Epoch 1273/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1242772.0529 - reconstruction_loss: 13385.4707 - vqvae_loss: 1229451.5481\n",
            "Epoch 1274/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1238615.3365 - reconstruction_loss: 13348.4277 - vqvae_loss: 1225313.1274\n",
            "Epoch 1275/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1239066.4976 - reconstruction_loss: 13392.5254 - vqvae_loss: 1225691.4207\n",
            "Epoch 1276/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1257883.3870 - reconstruction_loss: 13406.9414 - vqvae_loss: 1244407.2788\n",
            "Epoch 1277/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1236026.5000 - reconstruction_loss: 13390.2949 - vqvae_loss: 1222633.0841\n",
            "Epoch 1278/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1245298.3293 - reconstruction_loss: 13402.8857 - vqvae_loss: 1231934.3293\n",
            "Epoch 1279/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1254615.4111 - reconstruction_loss: 13534.9824 - vqvae_loss: 1241091.9351\n",
            "Epoch 1280/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1224771.5000 - reconstruction_loss: 13455.7930 - vqvae_loss: 1211381.6635\n",
            "Epoch 1281/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1261812.6130 - reconstruction_loss: 13488.8027 - vqvae_loss: 1248263.7981\n",
            "Epoch 1282/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1247633.0144 - reconstruction_loss: 13393.8311 - vqvae_loss: 1234312.9712\n",
            "Epoch 1283/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1224284.5409 - reconstruction_loss: 13405.2490 - vqvae_loss: 1210745.2260\n",
            "Epoch 1284/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1219791.7644 - reconstruction_loss: 13432.4268 - vqvae_loss: 1206360.9639\n",
            "Epoch 1285/2500\n",
            "51/51 [==============================] - 5s 89ms/step - loss: 1236026.1514 - reconstruction_loss: 13434.0234 - vqvae_loss: 1222468.0553\n",
            "Epoch 1286/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1228968.5481 - reconstruction_loss: 13438.9922 - vqvae_loss: 1215444.4038\n",
            "Epoch 1287/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1217000.2596 - reconstruction_loss: 13453.8418 - vqvae_loss: 1203529.8918\n",
            "Epoch 1288/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1232384.6755 - reconstruction_loss: 13428.3271 - vqvae_loss: 1218793.7716\n",
            "Epoch 1289/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1225442.2332 - reconstruction_loss: 13478.5107 - vqvae_loss: 1212045.7091\n",
            "Epoch 1290/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1216576.4447 - reconstruction_loss: 13394.6572 - vqvae_loss: 1203263.7620\n",
            "Epoch 1291/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1205672.7115 - reconstruction_loss: 13418.5713 - vqvae_loss: 1192332.0529\n",
            "Epoch 1292/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1218675.5216 - reconstruction_loss: 13415.7070 - vqvae_loss: 1205236.3101\n",
            "Epoch 1293/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1209517.1755 - reconstruction_loss: 13421.0703 - vqvae_loss: 1196070.8894\n",
            "Epoch 1294/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1220177.0312 - reconstruction_loss: 13487.8135 - vqvae_loss: 1206740.4543\n",
            "Epoch 1295/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1240907.9976 - reconstruction_loss: 13415.6094 - vqvae_loss: 1227338.6466\n",
            "Epoch 1296/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 1206055.4495 - reconstruction_loss: 13494.5273 - vqvae_loss: 1192526.1082\n",
            "Epoch 1297/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1220068.9567 - reconstruction_loss: 13444.6104 - vqvae_loss: 1206544.3197\n",
            "Epoch 1298/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1235602.9375 - reconstruction_loss: 13476.0479 - vqvae_loss: 1222139.2861\n",
            "Epoch 1299/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1209531.8221 - reconstruction_loss: 13405.9121 - vqvae_loss: 1196194.8341\n",
            "Epoch 1300/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1213332.3486 - reconstruction_loss: 13462.1729 - vqvae_loss: 1199859.9207\n",
            "Epoch 1301/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1200341.0841 - reconstruction_loss: 13444.3037 - vqvae_loss: 1186876.2716\n",
            "Epoch 1302/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1220996.1442 - reconstruction_loss: 13446.0449 - vqvae_loss: 1207505.0264\n",
            "Epoch 1303/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1207774.0048 - reconstruction_loss: 13447.9053 - vqvae_loss: 1194308.1082\n",
            "Epoch 1304/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1196268.0072 - reconstruction_loss: 13420.4170 - vqvae_loss: 1182923.1683\n",
            "Epoch 1305/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1219675.0481 - reconstruction_loss: 13470.0049 - vqvae_loss: 1206225.9976\n",
            "Epoch 1306/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1205116.8870 - reconstruction_loss: 13483.1484 - vqvae_loss: 1191719.3365\n",
            "Epoch 1307/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1192333.2957 - reconstruction_loss: 13496.0420 - vqvae_loss: 1178813.6418\n",
            "Epoch 1308/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1209097.8606 - reconstruction_loss: 13469.6104 - vqvae_loss: 1195586.8510\n",
            "Epoch 1309/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1189558.0264 - reconstruction_loss: 13469.5039 - vqvae_loss: 1176144.4087\n",
            "Epoch 1310/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1184426.2212 - reconstruction_loss: 13422.5039 - vqvae_loss: 1170989.3750\n",
            "Epoch 1311/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1187854.0168 - reconstruction_loss: 13402.8799 - vqvae_loss: 1174494.4351\n",
            "Epoch 1312/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1191294.6490 - reconstruction_loss: 13436.6621 - vqvae_loss: 1177894.7380\n",
            "Epoch 1313/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1180146.6611 - reconstruction_loss: 13389.7031 - vqvae_loss: 1166632.4375\n",
            "Epoch 1314/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1180969.3245 - reconstruction_loss: 13415.9668 - vqvae_loss: 1167607.3582\n",
            "Epoch 1315/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1178310.4375 - reconstruction_loss: 13413.0566 - vqvae_loss: 1164816.9231\n",
            "Epoch 1316/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1169413.8558 - reconstruction_loss: 13434.8574 - vqvae_loss: 1156091.0048\n",
            "Epoch 1317/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1177327.0817 - reconstruction_loss: 13438.4229 - vqvae_loss: 1163767.3510\n",
            "Epoch 1318/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1188536.8534 - reconstruction_loss: 13460.2295 - vqvae_loss: 1175042.9087\n",
            "Epoch 1319/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1175466.6250 - reconstruction_loss: 13415.2920 - vqvae_loss: 1162059.0409\n",
            "Epoch 1320/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1193654.0841 - reconstruction_loss: 13563.3799 - vqvae_loss: 1180134.3846\n",
            "Epoch 1321/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1185838.1683 - reconstruction_loss: 13458.2119 - vqvae_loss: 1172380.5433\n",
            "Epoch 1322/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1183582.7428 - reconstruction_loss: 13455.9482 - vqvae_loss: 1170115.5409\n",
            "Epoch 1323/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1181771.6779 - reconstruction_loss: 13488.8799 - vqvae_loss: 1168348.3365\n",
            "Epoch 1324/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1173302.6082 - reconstruction_loss: 13467.5479 - vqvae_loss: 1159816.7476\n",
            "Epoch 1325/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1186764.1418 - reconstruction_loss: 13514.4951 - vqvae_loss: 1173236.6394\n",
            "Epoch 1326/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1178036.4712 - reconstruction_loss: 13508.4941 - vqvae_loss: 1164556.6226\n",
            "Epoch 1327/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1190330.9832 - reconstruction_loss: 13520.4766 - vqvae_loss: 1176768.9279\n",
            "Epoch 1328/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1189138.9832 - reconstruction_loss: 13479.6455 - vqvae_loss: 1175521.7909\n",
            "Epoch 1329/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1178689.5649 - reconstruction_loss: 13459.6982 - vqvae_loss: 1165245.6562\n",
            "Epoch 1330/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1188844.3726 - reconstruction_loss: 13606.9873 - vqvae_loss: 1175147.6082\n",
            "Epoch 1331/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1171477.2524 - reconstruction_loss: 13475.7471 - vqvae_loss: 1157983.5288\n",
            "Epoch 1332/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1162572.3606 - reconstruction_loss: 13426.0391 - vqvae_loss: 1149155.6394\n",
            "Epoch 1333/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1151679.1178 - reconstruction_loss: 13419.8936 - vqvae_loss: 1138371.9856\n",
            "Epoch 1334/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1158086.1082 - reconstruction_loss: 13383.0391 - vqvae_loss: 1144736.6827\n",
            "Epoch 1335/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1155404.1298 - reconstruction_loss: 13438.5771 - vqvae_loss: 1142134.1947\n",
            "Epoch 1336/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1157103.0938 - reconstruction_loss: 13455.8887 - vqvae_loss: 1143766.3606\n",
            "Epoch 1337/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1143455.1322 - reconstruction_loss: 13463.5850 - vqvae_loss: 1130028.2596\n",
            "Epoch 1338/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1163005.6611 - reconstruction_loss: 13446.2402 - vqvae_loss: 1149475.4808\n",
            "Epoch 1339/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1161924.7115 - reconstruction_loss: 13406.4229 - vqvae_loss: 1148564.7548\n",
            "Epoch 1340/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1150716.4663 - reconstruction_loss: 13388.1543 - vqvae_loss: 1137263.6058\n",
            "Epoch 1341/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1175165.6346 - reconstruction_loss: 13502.3926 - vqvae_loss: 1161771.5889\n",
            "Epoch 1342/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1173682.9014 - reconstruction_loss: 13520.3457 - vqvae_loss: 1160197.6034\n",
            "Epoch 1343/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 1159653.9567 - reconstruction_loss: 13466.5469 - vqvae_loss: 1146222.4736\n",
            "Epoch 1344/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1173269.6611 - reconstruction_loss: 13475.7988 - vqvae_loss: 1159733.6202\n",
            "Epoch 1345/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1150724.5721 - reconstruction_loss: 13389.2490 - vqvae_loss: 1137349.0168\n",
            "Epoch 1346/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1159951.0986 - reconstruction_loss: 13431.9385 - vqvae_loss: 1146525.4976\n",
            "Epoch 1347/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1153298.5721 - reconstruction_loss: 13456.9131 - vqvae_loss: 1139996.9615\n",
            "Epoch 1348/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1162475.2163 - reconstruction_loss: 13494.1006 - vqvae_loss: 1148828.4736\n",
            "Epoch 1349/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1176201.1082 - reconstruction_loss: 13602.5146 - vqvae_loss: 1162631.5312\n",
            "Epoch 1350/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1177559.0457 - reconstruction_loss: 13597.9482 - vqvae_loss: 1164010.2788\n",
            "Epoch 1351/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1148783.5841 - reconstruction_loss: 13457.4365 - vqvae_loss: 1135208.4447\n",
            "Epoch 1352/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1149183.9760 - reconstruction_loss: 13468.4473 - vqvae_loss: 1135721.2476\n",
            "Epoch 1353/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1183799.1370 - reconstruction_loss: 13471.4727 - vqvae_loss: 1170289.1851\n",
            "Epoch 1354/2500\n",
            "51/51 [==============================] - 5s 89ms/step - loss: 1164106.2260 - reconstruction_loss: 13403.5537 - vqvae_loss: 1150561.5144\n",
            "Epoch 1355/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1136098.9495 - reconstruction_loss: 13412.3418 - vqvae_loss: 1122681.9159\n",
            "Epoch 1356/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1134007.5938 - reconstruction_loss: 13414.9990 - vqvae_loss: 1120510.1034\n",
            "Epoch 1357/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1140887.0481 - reconstruction_loss: 13451.3037 - vqvae_loss: 1127538.1250\n",
            "Epoch 1358/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1136558.1490 - reconstruction_loss: 13406.5527 - vqvae_loss: 1123173.7212\n",
            "Epoch 1359/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1130190.1298 - reconstruction_loss: 13468.7803 - vqvae_loss: 1116807.4231\n",
            "Epoch 1360/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1120192.3990 - reconstruction_loss: 13377.4922 - vqvae_loss: 1106814.0192\n",
            "Epoch 1361/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1112532.3077 - reconstruction_loss: 13450.9629 - vqvae_loss: 1099011.5649\n",
            "Epoch 1362/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1114577.0120 - reconstruction_loss: 13351.7031 - vqvae_loss: 1101103.1382\n",
            "Epoch 1363/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1119793.3125 - reconstruction_loss: 13414.2803 - vqvae_loss: 1106291.5769\n",
            "Epoch 1364/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1121569.6346 - reconstruction_loss: 13401.5088 - vqvae_loss: 1108184.9808\n",
            "Epoch 1365/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1100767.0625 - reconstruction_loss: 13436.6475 - vqvae_loss: 1087242.1971\n",
            "Epoch 1366/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1108151.7139 - reconstruction_loss: 13466.2021 - vqvae_loss: 1094710.8486\n",
            "Epoch 1367/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1100680.0889 - reconstruction_loss: 13443.2676 - vqvae_loss: 1087306.2139\n",
            "Epoch 1368/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1116569.2139 - reconstruction_loss: 13449.6963 - vqvae_loss: 1103007.9712\n",
            "Epoch 1369/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1131469.1514 - reconstruction_loss: 13476.7588 - vqvae_loss: 1117984.5745\n",
            "Epoch 1370/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1124772.9928 - reconstruction_loss: 13460.7217 - vqvae_loss: 1111141.2332\n",
            "Epoch 1371/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1136347.6827 - reconstruction_loss: 13541.9502 - vqvae_loss: 1122820.7548\n",
            "Epoch 1372/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1126223.1370 - reconstruction_loss: 13510.2734 - vqvae_loss: 1112689.6707\n",
            "Epoch 1373/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1114641.4808 - reconstruction_loss: 13481.4521 - vqvae_loss: 1101267.9760\n",
            "Epoch 1374/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1106207.8413 - reconstruction_loss: 13411.9863 - vqvae_loss: 1092973.4111\n",
            "Epoch 1375/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1087120.1803 - reconstruction_loss: 13508.6982 - vqvae_loss: 1073579.7308\n",
            "Epoch 1376/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1070456.0901 - reconstruction_loss: 13512.1455 - vqvae_loss: 1057091.3894\n",
            "Epoch 1377/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1082307.2308 - reconstruction_loss: 13469.6914 - vqvae_loss: 1068745.0901\n",
            "Epoch 1378/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1070213.8546 - reconstruction_loss: 13484.5928 - vqvae_loss: 1056712.5613\n",
            "Epoch 1379/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1087435.0769 - reconstruction_loss: 13448.1982 - vqvae_loss: 1073899.0024\n",
            "Epoch 1380/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1073196.6406 - reconstruction_loss: 13366.8711 - vqvae_loss: 1059853.3113\n",
            "Epoch 1381/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1081151.5361 - reconstruction_loss: 13491.9697 - vqvae_loss: 1067450.0829\n",
            "Epoch 1382/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1063505.3786 - reconstruction_loss: 13457.1016 - vqvae_loss: 1049990.8353\n",
            "Epoch 1383/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1067876.1863 - reconstruction_loss: 13388.2559 - vqvae_loss: 1054484.6947\n",
            "Epoch 1384/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1067492.7837 - reconstruction_loss: 13426.5547 - vqvae_loss: 1054051.6142\n",
            "Epoch 1385/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1078339.2933 - reconstruction_loss: 13468.2021 - vqvae_loss: 1064728.2212\n",
            "Epoch 1386/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1061074.2692 - reconstruction_loss: 13307.8779 - vqvae_loss: 1047711.0553\n",
            "Epoch 1387/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1049441.0108 - reconstruction_loss: 13380.0537 - vqvae_loss: 1036114.9255\n",
            "Epoch 1388/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1070031.6827 - reconstruction_loss: 13432.6074 - vqvae_loss: 1056607.3365\n",
            "Epoch 1389/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1079577.0337 - reconstruction_loss: 13436.7217 - vqvae_loss: 1066029.7416\n",
            "Epoch 1390/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1068938.0312 - reconstruction_loss: 13513.0674 - vqvae_loss: 1055303.6839\n",
            "Epoch 1391/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1088451.9183 - reconstruction_loss: 13510.1982 - vqvae_loss: 1074798.6046\n",
            "Epoch 1392/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1039662.4002 - reconstruction_loss: 13298.3857 - vqvae_loss: 1026358.2200\n",
            "Epoch 1393/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1039236.5637 - reconstruction_loss: 13308.5752 - vqvae_loss: 1026010.3498\n",
            "Epoch 1394/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1054456.1178 - reconstruction_loss: 13361.0830 - vqvae_loss: 1041055.1611\n",
            "Epoch 1395/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1068462.1466 - reconstruction_loss: 13504.9180 - vqvae_loss: 1055010.4255\n",
            "Epoch 1396/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1081248.6274 - reconstruction_loss: 13492.8232 - vqvae_loss: 1067763.6226\n",
            "Epoch 1397/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 1077454.0721 - reconstruction_loss: 13483.3711 - vqvae_loss: 1064050.9291\n",
            "Epoch 1398/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1075758.4663 - reconstruction_loss: 13491.1885 - vqvae_loss: 1062241.5252\n",
            "Epoch 1399/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1046572.9447 - reconstruction_loss: 13373.2578 - vqvae_loss: 1033139.9351\n",
            "Epoch 1400/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1043498.8930 - reconstruction_loss: 13400.7939 - vqvae_loss: 1029997.8293\n",
            "Epoch 1401/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1032414.6623 - reconstruction_loss: 13389.9912 - vqvae_loss: 1019024.3714\n",
            "Epoch 1402/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1049079.6274 - reconstruction_loss: 13453.8623 - vqvae_loss: 1035510.7620\n",
            "Epoch 1403/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1054560.7188 - reconstruction_loss: 13521.1201 - vqvae_loss: 1040967.1130\n",
            "Epoch 1404/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1052886.8870 - reconstruction_loss: 13445.4805 - vqvae_loss: 1039465.3918\n",
            "Epoch 1405/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1048296.1611 - reconstruction_loss: 13516.2168 - vqvae_loss: 1034768.3834\n",
            "Epoch 1406/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1052806.0300 - reconstruction_loss: 13539.4082 - vqvae_loss: 1039289.0433\n",
            "Epoch 1407/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1055897.0120 - reconstruction_loss: 13480.9922 - vqvae_loss: 1042244.2368\n",
            "Epoch 1408/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1052961.1611 - reconstruction_loss: 13488.4150 - vqvae_loss: 1039450.1959\n",
            "Epoch 1409/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1036117.2584 - reconstruction_loss: 13473.5684 - vqvae_loss: 1022606.1995\n",
            "Epoch 1410/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1049763.8486 - reconstruction_loss: 13483.5859 - vqvae_loss: 1036159.5625\n",
            "Epoch 1411/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1022544.7668 - reconstruction_loss: 13445.3809 - vqvae_loss: 1008962.5577\n",
            "Epoch 1412/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1021475.6623 - reconstruction_loss: 13568.3125 - vqvae_loss: 1007822.7800\n",
            "Epoch 1413/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 1024003.6226 - reconstruction_loss: 13517.7627 - vqvae_loss: 1010448.8990\n",
            "Epoch 1414/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1017467.4267 - reconstruction_loss: 13471.4873 - vqvae_loss: 1003880.3257\n",
            "Epoch 1415/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1006199.6286 - reconstruction_loss: 13468.2021 - vqvae_loss: 992620.6683\n",
            "Epoch 1416/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 984897.6659 - reconstruction_loss: 13418.8701 - vqvae_loss: 971491.3942\n",
            "Epoch 1417/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 999702.3401 - reconstruction_loss: 13440.5635 - vqvae_loss: 986326.2224\n",
            "Epoch 1418/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 994350.3582 - reconstruction_loss: 13489.2373 - vqvae_loss: 981012.9748\n",
            "Epoch 1419/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1002473.7404 - reconstruction_loss: 13485.2246 - vqvae_loss: 989084.7440\n",
            "Epoch 1420/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1017183.0204 - reconstruction_loss: 13381.5977 - vqvae_loss: 1003821.8413\n",
            "Epoch 1421/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 994755.4688 - reconstruction_loss: 13409.7168 - vqvae_loss: 981427.5445\n",
            "Epoch 1422/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 996207.7897 - reconstruction_loss: 13499.2324 - vqvae_loss: 982725.3870\n",
            "Epoch 1423/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1012834.7945 - reconstruction_loss: 13454.4023 - vqvae_loss: 999464.8906\n",
            "Epoch 1424/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1002542.8269 - reconstruction_loss: 13370.2402 - vqvae_loss: 989282.9988\n",
            "Epoch 1425/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 1018963.9050 - reconstruction_loss: 13407.0000 - vqvae_loss: 1005520.7596\n",
            "Epoch 1426/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1016101.5192 - reconstruction_loss: 13475.9717 - vqvae_loss: 1002497.3305\n",
            "Epoch 1427/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1003003.8942 - reconstruction_loss: 13505.3896 - vqvae_loss: 989569.2007\n",
            "Epoch 1428/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1004889.0998 - reconstruction_loss: 13461.6328 - vqvae_loss: 991410.0288\n",
            "Epoch 1429/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1012832.2620 - reconstruction_loss: 13538.2998 - vqvae_loss: 999178.9399\n",
            "Epoch 1430/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 1007665.1731 - reconstruction_loss: 13511.0127 - vqvae_loss: 994042.4195\n",
            "Epoch 1431/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 992191.6322 - reconstruction_loss: 13517.3311 - vqvae_loss: 978574.4567\n",
            "Epoch 1432/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 992502.6358 - reconstruction_loss: 13509.7803 - vqvae_loss: 978898.5361\n",
            "Epoch 1433/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 986559.9543 - reconstruction_loss: 13568.6455 - vqvae_loss: 972821.5925\n",
            "Epoch 1434/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 985451.3858 - reconstruction_loss: 13608.9150 - vqvae_loss: 971949.7680\n",
            "Epoch 1435/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 988254.8474 - reconstruction_loss: 13584.1074 - vqvae_loss: 974632.3858\n",
            "Epoch 1436/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 983498.4339 - reconstruction_loss: 13621.3408 - vqvae_loss: 970036.7212\n",
            "Epoch 1437/2500\n",
            "51/51 [==============================] - 5s 103ms/step - loss: 993667.2356 - reconstruction_loss: 13457.6357 - vqvae_loss: 980362.0709\n",
            "Epoch 1438/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 990035.6635 - reconstruction_loss: 13566.8623 - vqvae_loss: 976690.1214\n",
            "Epoch 1439/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 1011084.1190 - reconstruction_loss: 13543.1279 - vqvae_loss: 997460.9820\n",
            "Epoch 1440/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 998197.5240 - reconstruction_loss: 13513.9102 - vqvae_loss: 984753.4844\n",
            "Epoch 1441/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 1016929.6623 - reconstruction_loss: 13522.0547 - vqvae_loss: 1003286.7812\n",
            "Epoch 1442/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 989625.4832 - reconstruction_loss: 13450.0322 - vqvae_loss: 976140.2536\n",
            "Epoch 1443/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 992449.8918 - reconstruction_loss: 13630.9336 - vqvae_loss: 978734.0793\n",
            "Epoch 1444/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 997404.8341 - reconstruction_loss: 13549.7627 - vqvae_loss: 983683.3486\n",
            "Epoch 1445/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 981605.0877 - reconstruction_loss: 13524.1582 - vqvae_loss: 968009.6526\n",
            "Epoch 1446/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 954461.8365 - reconstruction_loss: 13519.9561 - vqvae_loss: 940884.2031\n",
            "Epoch 1447/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 937367.9627 - reconstruction_loss: 13489.8252 - vqvae_loss: 923913.1058\n",
            "Epoch 1448/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 951440.8450 - reconstruction_loss: 13548.3691 - vqvae_loss: 938077.1238\n",
            "Epoch 1449/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 955997.6274 - reconstruction_loss: 13534.3740 - vqvae_loss: 942659.0120\n",
            "Epoch 1450/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 955750.4267 - reconstruction_loss: 13380.4453 - vqvae_loss: 942392.7608\n",
            "Epoch 1451/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 954536.8425 - reconstruction_loss: 13471.1104 - vqvae_loss: 941160.7764\n",
            "Epoch 1452/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 969224.2043 - reconstruction_loss: 13496.4238 - vqvae_loss: 955689.2055\n",
            "Epoch 1453/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 967269.2812 - reconstruction_loss: 13456.7207 - vqvae_loss: 953703.8077\n",
            "Epoch 1454/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 969462.1370 - reconstruction_loss: 13438.9629 - vqvae_loss: 955893.8726\n",
            "Epoch 1455/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 948805.9471 - reconstruction_loss: 13477.6797 - vqvae_loss: 935287.6623\n",
            "Epoch 1456/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 941274.6947 - reconstruction_loss: 13509.7461 - vqvae_loss: 927756.5757\n",
            "Epoch 1457/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 918629.3209 - reconstruction_loss: 13432.7949 - vqvae_loss: 905263.5901\n",
            "Epoch 1458/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 925715.4387 - reconstruction_loss: 13436.6162 - vqvae_loss: 912512.3474\n",
            "Epoch 1459/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 941939.7440 - reconstruction_loss: 13451.5996 - vqvae_loss: 928642.0613\n",
            "Epoch 1460/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 961042.7452 - reconstruction_loss: 13482.8799 - vqvae_loss: 947668.4423\n",
            "Epoch 1461/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 970157.9087 - reconstruction_loss: 13598.4268 - vqvae_loss: 956591.6635\n",
            "Epoch 1462/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 968824.4147 - reconstruction_loss: 13400.5371 - vqvae_loss: 955280.4916\n",
            "Epoch 1463/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 937316.4675 - reconstruction_loss: 13322.6904 - vqvae_loss: 923869.9447\n",
            "Epoch 1464/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 916331.6478 - reconstruction_loss: 13387.7393 - vqvae_loss: 902880.9279\n",
            "Epoch 1465/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 906852.0661 - reconstruction_loss: 13376.5000 - vqvae_loss: 893392.1647\n",
            "Epoch 1466/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 905584.7632 - reconstruction_loss: 13376.4131 - vqvae_loss: 892238.9291\n",
            "Epoch 1467/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 909238.3750 - reconstruction_loss: 13473.8896 - vqvae_loss: 895687.9784\n",
            "Epoch 1468/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 932167.7308 - reconstruction_loss: 13399.2129 - vqvae_loss: 918845.0613\n",
            "Epoch 1469/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 937611.5541 - reconstruction_loss: 13426.6279 - vqvae_loss: 924076.5565\n",
            "Epoch 1470/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 948001.2897 - reconstruction_loss: 13425.4238 - vqvae_loss: 934537.1286\n",
            "Epoch 1471/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 945525.9772 - reconstruction_loss: 13375.9092 - vqvae_loss: 932147.5084\n",
            "Epoch 1472/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 929775.4603 - reconstruction_loss: 13350.8379 - vqvae_loss: 916413.1803\n",
            "Epoch 1473/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 917741.0661 - reconstruction_loss: 13342.3467 - vqvae_loss: 904271.0120\n",
            "Epoch 1474/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 904428.7163 - reconstruction_loss: 13405.8838 - vqvae_loss: 890952.9892\n",
            "Epoch 1475/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 903621.3041 - reconstruction_loss: 13403.8975 - vqvae_loss: 890192.2236\n",
            "Epoch 1476/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 904187.7236 - reconstruction_loss: 13402.3740 - vqvae_loss: 890827.7560\n",
            "Epoch 1477/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 907607.8822 - reconstruction_loss: 13311.4922 - vqvae_loss: 894374.9243\n",
            "Epoch 1478/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 902831.5817 - reconstruction_loss: 13381.2900 - vqvae_loss: 889444.3041\n",
            "Epoch 1479/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 921485.6430 - reconstruction_loss: 13440.4141 - vqvae_loss: 908093.2356\n",
            "Epoch 1480/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 911061.8642 - reconstruction_loss: 13396.2979 - vqvae_loss: 897826.0517\n",
            "Epoch 1481/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 931115.7260 - reconstruction_loss: 13350.5957 - vqvae_loss: 917764.6514\n",
            "Epoch 1482/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 915737.3113 - reconstruction_loss: 13402.3047 - vqvae_loss: 902347.0132\n",
            "Epoch 1483/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 924339.0601 - reconstruction_loss: 13351.0137 - vqvae_loss: 910985.9832\n",
            "Epoch 1484/2500\n",
            "51/51 [==============================] - 5s 101ms/step - loss: 909139.5385 - reconstruction_loss: 13374.5576 - vqvae_loss: 895751.8762\n",
            "Epoch 1485/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 930817.9952 - reconstruction_loss: 13420.8545 - vqvae_loss: 917310.0589\n",
            "Epoch 1486/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 897802.5132 - reconstruction_loss: 13431.1250 - vqvae_loss: 884246.7812\n",
            "Epoch 1487/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 889184.1875 - reconstruction_loss: 13329.1934 - vqvae_loss: 875660.3546\n",
            "Epoch 1488/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 873129.1827 - reconstruction_loss: 13337.1826 - vqvae_loss: 859689.2764\n",
            "Epoch 1489/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 879761.1214 - reconstruction_loss: 13435.9756 - vqvae_loss: 866374.3065\n",
            "Epoch 1490/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 882954.4712 - reconstruction_loss: 13327.8623 - vqvae_loss: 869558.9279\n",
            "Epoch 1491/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 876886.5036 - reconstruction_loss: 13330.2920 - vqvae_loss: 863638.6214\n",
            "Epoch 1492/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 899946.0673 - reconstruction_loss: 13437.3184 - vqvae_loss: 886560.1262\n",
            "Epoch 1493/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 906095.3365 - reconstruction_loss: 13306.5088 - vqvae_loss: 892725.2909\n",
            "Epoch 1494/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 895708.2536 - reconstruction_loss: 13427.2686 - vqvae_loss: 882372.6466\n",
            "Epoch 1495/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 907023.5817 - reconstruction_loss: 13353.6445 - vqvae_loss: 893665.1683\n",
            "Epoch 1496/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 891532.3305 - reconstruction_loss: 13365.2197 - vqvae_loss: 878190.1502\n",
            "Epoch 1497/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 892798.9159 - reconstruction_loss: 13356.3086 - vqvae_loss: 879287.6406\n",
            "Epoch 1498/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 864368.5745 - reconstruction_loss: 13407.4619 - vqvae_loss: 851021.7524\n",
            "Epoch 1499/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 862174.3017 - reconstruction_loss: 13355.6631 - vqvae_loss: 848801.2837\n",
            "Epoch 1500/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 860986.9171 - reconstruction_loss: 13464.0039 - vqvae_loss: 847555.7224\n",
            "Epoch 1501/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 860806.9303 - reconstruction_loss: 13415.8086 - vqvae_loss: 847449.0901\n",
            "Epoch 1502/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 866085.9411 - reconstruction_loss: 13417.1904 - vqvae_loss: 852705.6923\n",
            "Epoch 1503/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 889354.6599 - reconstruction_loss: 13429.6777 - vqvae_loss: 876085.8401\n",
            "Epoch 1504/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 876972.7957 - reconstruction_loss: 13336.7793 - vqvae_loss: 863697.9171\n",
            "Epoch 1505/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 877030.1743 - reconstruction_loss: 13321.0752 - vqvae_loss: 863810.6587\n",
            "Epoch 1506/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 883210.5457 - reconstruction_loss: 13341.3174 - vqvae_loss: 869983.7945\n",
            "Epoch 1507/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 895120.6599 - reconstruction_loss: 13416.4854 - vqvae_loss: 881757.9243\n",
            "Epoch 1508/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 914932.8762 - reconstruction_loss: 13379.9795 - vqvae_loss: 901615.3317\n",
            "Epoch 1509/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 886186.9062 - reconstruction_loss: 13348.0576 - vqvae_loss: 872712.8353\n",
            "Epoch 1510/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 872305.5805 - reconstruction_loss: 13464.7510 - vqvae_loss: 858693.6250\n",
            "Epoch 1511/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 865458.3990 - reconstruction_loss: 13376.7051 - vqvae_loss: 851985.0288\n",
            "Epoch 1512/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 851237.7668 - reconstruction_loss: 13321.2559 - vqvae_loss: 837902.5901\n",
            "Epoch 1513/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 843602.4808 - reconstruction_loss: 13353.3955 - vqvae_loss: 830141.6659\n",
            "Epoch 1514/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 856244.6058 - reconstruction_loss: 13402.6641 - vqvae_loss: 842909.1587\n",
            "Epoch 1515/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 858168.6827 - reconstruction_loss: 13364.9473 - vqvae_loss: 844678.0337\n",
            "Epoch 1516/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 853702.2416 - reconstruction_loss: 13456.4424 - vqvae_loss: 840388.1647\n",
            "Epoch 1517/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 878953.4026 - reconstruction_loss: 13344.5244 - vqvae_loss: 865706.1166\n",
            "Epoch 1518/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 870928.8029 - reconstruction_loss: 13349.7695 - vqvae_loss: 857504.7560\n",
            "Epoch 1519/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 884331.3221 - reconstruction_loss: 13351.3018 - vqvae_loss: 870888.1971\n",
            "Epoch 1520/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 864235.6839 - reconstruction_loss: 13330.2695 - vqvae_loss: 850819.2248\n",
            "Epoch 1521/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 866518.1382 - reconstruction_loss: 13359.3154 - vqvae_loss: 853024.7849\n",
            "Epoch 1522/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 860368.1322 - reconstruction_loss: 13371.0947 - vqvae_loss: 846839.9159\n",
            "Epoch 1523/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 844277.6659 - reconstruction_loss: 13447.8135 - vqvae_loss: 830839.2488\n",
            "Epoch 1524/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 838141.1971 - reconstruction_loss: 13430.0674 - vqvae_loss: 824749.0288\n",
            "Epoch 1525/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 838074.0252 - reconstruction_loss: 13402.1885 - vqvae_loss: 824700.1022\n",
            "Epoch 1526/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 841920.8906 - reconstruction_loss: 13322.5303 - vqvae_loss: 828698.7933\n",
            "Epoch 1527/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 851732.2560 - reconstruction_loss: 13367.1670 - vqvae_loss: 838600.4279\n",
            "Epoch 1528/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 873073.2055 - reconstruction_loss: 13390.4561 - vqvae_loss: 859749.6478\n",
            "Epoch 1529/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 848218.0096 - reconstruction_loss: 13313.2646 - vqvae_loss: 835038.1635\n",
            "Epoch 1530/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 878130.1478 - reconstruction_loss: 13403.9268 - vqvae_loss: 864816.5841\n",
            "Epoch 1531/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 887585.0493 - reconstruction_loss: 13418.8574 - vqvae_loss: 874015.7825\n",
            "Epoch 1532/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 847734.7332 - reconstruction_loss: 13341.5918 - vqvae_loss: 834218.3113\n",
            "Epoch 1533/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 847661.0781 - reconstruction_loss: 13355.4326 - vqvae_loss: 834175.7740\n",
            "Epoch 1534/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 840896.3894 - reconstruction_loss: 13527.0820 - vqvae_loss: 827286.4579\n",
            "Epoch 1535/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 825736.7524 - reconstruction_loss: 13418.7998 - vqvae_loss: 812311.9856\n",
            "Epoch 1536/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 830642.2692 - reconstruction_loss: 13488.4619 - vqvae_loss: 817366.3510\n",
            "Epoch 1537/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 870052.7776 - reconstruction_loss: 13418.7705 - vqvae_loss: 856558.4111\n",
            "Epoch 1538/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 851127.9050 - reconstruction_loss: 13307.9453 - vqvae_loss: 837735.5986\n",
            "Epoch 1539/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 853380.2596 - reconstruction_loss: 13350.1104 - vqvae_loss: 840082.5577\n",
            "Epoch 1540/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 858502.3209 - reconstruction_loss: 13313.1152 - vqvae_loss: 845075.4736\n",
            "Epoch 1541/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 842601.5962 - reconstruction_loss: 13326.9102 - vqvae_loss: 829296.1731\n",
            "Epoch 1542/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 834427.5962 - reconstruction_loss: 13334.5732 - vqvae_loss: 821077.8077\n",
            "Epoch 1543/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 826874.5517 - reconstruction_loss: 13292.0186 - vqvae_loss: 813528.9339\n",
            "Epoch 1544/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 828274.7236 - reconstruction_loss: 13405.6533 - vqvae_loss: 814779.0120\n",
            "Epoch 1545/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 828693.9002 - reconstruction_loss: 13434.3545 - vqvae_loss: 815335.9423\n",
            "Epoch 1546/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 827600.6935 - reconstruction_loss: 13344.9824 - vqvae_loss: 814301.0889\n",
            "Epoch 1547/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 835462.1286 - reconstruction_loss: 13327.0811 - vqvae_loss: 822201.7332\n",
            "Epoch 1548/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 834835.1731 - reconstruction_loss: 13281.4131 - vqvae_loss: 821634.2296\n",
            "Epoch 1549/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 838668.3425 - reconstruction_loss: 13380.0039 - vqvae_loss: 825491.4171\n",
            "Epoch 1550/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 862510.4459 - reconstruction_loss: 13463.7656 - vqvae_loss: 849091.1611\n",
            "Epoch 1551/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 842395.6971 - reconstruction_loss: 13345.9561 - vqvae_loss: 828976.4459\n",
            "Epoch 1552/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 854461.8762 - reconstruction_loss: 13304.1650 - vqvae_loss: 841063.5757\n",
            "Epoch 1553/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 839119.8630 - reconstruction_loss: 13364.9375 - vqvae_loss: 825562.7728\n",
            "Epoch 1554/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 820239.3654 - reconstruction_loss: 13409.1211 - vqvae_loss: 806752.2320\n",
            "Epoch 1555/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 831681.7175 - reconstruction_loss: 13488.9873 - vqvae_loss: 818068.3978\n",
            "Epoch 1556/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 830508.5216 - reconstruction_loss: 13343.1572 - vqvae_loss: 817165.7656\n",
            "Epoch 1557/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 828414.2776 - reconstruction_loss: 13320.7197 - vqvae_loss: 815278.6719\n",
            "Epoch 1558/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 849691.1022 - reconstruction_loss: 13278.3125 - vqvae_loss: 836363.1911\n",
            "Epoch 1559/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 847460.4627 - reconstruction_loss: 13242.0820 - vqvae_loss: 834185.4423\n",
            "Epoch 1560/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 840072.2800 - reconstruction_loss: 13258.0654 - vqvae_loss: 826783.5457\n",
            "Epoch 1561/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 836264.4062 - reconstruction_loss: 13302.2783 - vqvae_loss: 822859.8089\n",
            "Epoch 1562/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 816683.9784 - reconstruction_loss: 13261.1260 - vqvae_loss: 803391.6599\n",
            "Epoch 1563/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 813906.2656 - reconstruction_loss: 13340.9424 - vqvae_loss: 800538.3594\n",
            "Epoch 1564/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 824855.3353 - reconstruction_loss: 13309.3320 - vqvae_loss: 811520.1755\n",
            "Epoch 1565/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 840516.9147 - reconstruction_loss: 13300.4512 - vqvae_loss: 827351.0493\n",
            "Epoch 1566/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 844085.9483 - reconstruction_loss: 13237.3789 - vqvae_loss: 830964.2608\n",
            "Epoch 1567/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 825946.5745 - reconstruction_loss: 13257.7705 - vqvae_loss: 812751.9399\n",
            "Epoch 1568/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 845643.2452 - reconstruction_loss: 13299.1650 - vqvae_loss: 832268.7680\n",
            "Epoch 1569/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 838050.9327 - reconstruction_loss: 13289.0205 - vqvae_loss: 824750.9423\n",
            "Epoch 1570/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 842705.0397 - reconstruction_loss: 13284.3525 - vqvae_loss: 829430.4159\n",
            "Epoch 1571/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 835532.0036 - reconstruction_loss: 13262.3223 - vqvae_loss: 822259.2368\n",
            "Epoch 1572/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 831208.5120 - reconstruction_loss: 13247.2705 - vqvae_loss: 817892.8053\n",
            "Epoch 1573/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 816397.4243 - reconstruction_loss: 13223.3506 - vqvae_loss: 803163.7368\n",
            "Epoch 1574/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 811262.7019 - reconstruction_loss: 13246.9717 - vqvae_loss: 798037.1238\n",
            "Epoch 1575/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 813659.5421 - reconstruction_loss: 13280.4102 - vqvae_loss: 800442.5938\n",
            "Epoch 1576/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 815510.4483 - reconstruction_loss: 13260.4990 - vqvae_loss: 802159.4928\n",
            "Epoch 1577/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 812977.6923 - reconstruction_loss: 13226.5352 - vqvae_loss: 799653.1923\n",
            "Epoch 1578/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 804238.7356 - reconstruction_loss: 13291.3643 - vqvae_loss: 790956.9135\n",
            "Epoch 1579/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808419.5060 - reconstruction_loss: 13260.3740 - vqvae_loss: 795112.4243\n",
            "Epoch 1580/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 814664.1502 - reconstruction_loss: 13243.3945 - vqvae_loss: 801435.7151\n",
            "Epoch 1581/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815725.2079 - reconstruction_loss: 13286.5879 - vqvae_loss: 802295.5841\n",
            "Epoch 1582/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 812368.6430 - reconstruction_loss: 13351.9414 - vqvae_loss: 799083.5817\n",
            "Epoch 1583/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 815305.2320 - reconstruction_loss: 13369.6074 - vqvae_loss: 801964.8522\n",
            "Epoch 1584/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 812355.1310 - reconstruction_loss: 13300.9326 - vqvae_loss: 799020.2115\n",
            "Epoch 1585/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 802097.6046 - reconstruction_loss: 13250.9746 - vqvae_loss: 788946.6587\n",
            "Epoch 1586/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 812448.6106 - reconstruction_loss: 13246.1553 - vqvae_loss: 799213.7296\n",
            "Epoch 1587/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 814117.5889 - reconstruction_loss: 13191.0303 - vqvae_loss: 800828.4267\n",
            "Epoch 1588/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 825313.8810 - reconstruction_loss: 13195.5059 - vqvae_loss: 812093.5901\n",
            "Epoch 1589/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 811996.7139 - reconstruction_loss: 13312.8994 - vqvae_loss: 798739.6815\n",
            "Epoch 1590/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 818302.6382 - reconstruction_loss: 13253.9580 - vqvae_loss: 804942.6659\n",
            "Epoch 1591/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 816738.3798 - reconstruction_loss: 13218.2627 - vqvae_loss: 803400.0276\n",
            "Epoch 1592/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 811363.1070 - reconstruction_loss: 13222.0127 - vqvae_loss: 798092.7284\n",
            "Epoch 1593/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 814667.5829 - reconstruction_loss: 13280.2637 - vqvae_loss: 801382.4748\n",
            "Epoch 1594/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 821069.9038 - reconstruction_loss: 13262.8652 - vqvae_loss: 807840.9639\n",
            "Epoch 1595/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 814342.3978 - reconstruction_loss: 13249.9971 - vqvae_loss: 800936.2981\n",
            "Epoch 1596/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 820083.8329 - reconstruction_loss: 13268.1582 - vqvae_loss: 806698.1983\n",
            "Epoch 1597/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 811900.0950 - reconstruction_loss: 13296.9912 - vqvae_loss: 798675.9796\n",
            "Epoch 1598/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 815984.9291 - reconstruction_loss: 13274.4570 - vqvae_loss: 802638.3846\n",
            "Epoch 1599/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 815732.6058 - reconstruction_loss: 13304.0977 - vqvae_loss: 802389.5998\n",
            "Epoch 1600/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 825907.8185 - reconstruction_loss: 13341.6689 - vqvae_loss: 812543.1334\n",
            "Epoch 1601/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 825994.7368 - reconstruction_loss: 13365.4512 - vqvae_loss: 812795.0877\n",
            "Epoch 1602/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 837730.3113 - reconstruction_loss: 13384.9531 - vqvae_loss: 824450.3642\n",
            "Epoch 1603/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 841360.9856 - reconstruction_loss: 13470.6982 - vqvae_loss: 827735.8918\n",
            "Epoch 1604/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 826121.7103 - reconstruction_loss: 13366.9199 - vqvae_loss: 812674.8185\n",
            "Epoch 1605/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 820844.6046 - reconstruction_loss: 13346.3096 - vqvae_loss: 807538.0433\n",
            "Epoch 1606/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 820825.1262 - reconstruction_loss: 13319.1748 - vqvae_loss: 807492.1779\n",
            "Epoch 1607/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826200.5397 - reconstruction_loss: 13339.6973 - vqvae_loss: 812949.0312\n",
            "Epoch 1608/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824472.4135 - reconstruction_loss: 13304.0000 - vqvae_loss: 811241.0192\n",
            "Epoch 1609/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 813461.5877 - reconstruction_loss: 13298.1025 - vqvae_loss: 800283.3365\n",
            "Epoch 1610/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 814549.3462 - reconstruction_loss: 13341.3906 - vqvae_loss: 801223.4411\n",
            "Epoch 1611/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 808595.2452 - reconstruction_loss: 13397.7119 - vqvae_loss: 795249.4087\n",
            "Epoch 1612/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 810903.1635 - reconstruction_loss: 13445.9756 - vqvae_loss: 797404.4639\n",
            "Epoch 1613/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 808699.4267 - reconstruction_loss: 13423.8662 - vqvae_loss: 795252.2332\n",
            "Epoch 1614/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 805060.2969 - reconstruction_loss: 13538.7031 - vqvae_loss: 791453.6899\n",
            "Epoch 1615/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 810310.0757 - reconstruction_loss: 13375.4180 - vqvae_loss: 796899.6575\n",
            "Epoch 1616/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812800.4736 - reconstruction_loss: 13480.2803 - vqvae_loss: 799239.3522\n",
            "Epoch 1617/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 809031.8474 - reconstruction_loss: 13383.1748 - vqvae_loss: 795560.1803\n",
            "Epoch 1618/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 809424.4639 - reconstruction_loss: 13426.1523 - vqvae_loss: 795924.9014\n",
            "Epoch 1619/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 803777.7921 - reconstruction_loss: 13405.4131 - vqvae_loss: 790252.4603\n",
            "Epoch 1620/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 803619.2320 - reconstruction_loss: 13424.4922 - vqvae_loss: 790283.2849\n",
            "Epoch 1621/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808271.3690 - reconstruction_loss: 13456.9346 - vqvae_loss: 794774.7885\n",
            "Epoch 1622/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800740.7356 - reconstruction_loss: 13427.0654 - vqvae_loss: 787228.4243\n",
            "Epoch 1623/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 804142.5673 - reconstruction_loss: 13293.9971 - vqvae_loss: 790931.5877\n",
            "Epoch 1624/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 798555.3666 - reconstruction_loss: 13308.6387 - vqvae_loss: 785180.7897\n",
            "Epoch 1625/2500\n",
            "51/51 [==============================] - 5s 88ms/step - loss: 801022.3305 - reconstruction_loss: 13320.5518 - vqvae_loss: 787702.5168\n",
            "Epoch 1626/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 803721.5216 - reconstruction_loss: 13441.7881 - vqvae_loss: 790344.7981\n",
            "Epoch 1627/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 811821.2043 - reconstruction_loss: 13446.5635 - vqvae_loss: 798386.2933\n",
            "Epoch 1628/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 790093.8942 - reconstruction_loss: 13336.6934 - vqvae_loss: 776762.8233\n",
            "Epoch 1629/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 814675.3870 - reconstruction_loss: 13318.3906 - vqvae_loss: 801354.6250\n",
            "Epoch 1630/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 816311.4411 - reconstruction_loss: 13260.9141 - vqvae_loss: 802984.7933\n",
            "Epoch 1631/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 812370.6370 - reconstruction_loss: 13355.5205 - vqvae_loss: 799025.3546\n",
            "Epoch 1632/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808892.4880 - reconstruction_loss: 13470.9512 - vqvae_loss: 795408.1286\n",
            "Epoch 1633/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 802435.8786 - reconstruction_loss: 13344.4463 - vqvae_loss: 789070.0950\n",
            "Epoch 1634/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808076.2007 - reconstruction_loss: 13344.3545 - vqvae_loss: 794585.3474\n",
            "Epoch 1635/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 806669.4351 - reconstruction_loss: 13475.4668 - vqvae_loss: 793296.5697\n",
            "Epoch 1636/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 816621.6466 - reconstruction_loss: 13474.6377 - vqvae_loss: 803137.1827\n",
            "Epoch 1637/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 810270.8438 - reconstruction_loss: 13520.6406 - vqvae_loss: 796743.0048\n",
            "Epoch 1638/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 804254.2524 - reconstruction_loss: 13471.6748 - vqvae_loss: 790653.8353\n",
            "Epoch 1639/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 797012.1214 - reconstruction_loss: 13292.5186 - vqvae_loss: 783748.0156\n",
            "Epoch 1640/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 811631.2957 - reconstruction_loss: 13294.2295 - vqvae_loss: 798253.1502\n",
            "Epoch 1641/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 802774.6010 - reconstruction_loss: 13320.7559 - vqvae_loss: 789502.5469\n",
            "Epoch 1642/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 805948.5998 - reconstruction_loss: 13385.3027 - vqvae_loss: 792613.8882\n",
            "Epoch 1643/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 803896.7668 - reconstruction_loss: 13418.6582 - vqvae_loss: 790592.4844\n",
            "Epoch 1644/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 787776.8125 - reconstruction_loss: 13334.0586 - vqvae_loss: 774442.0240\n",
            "Epoch 1645/2500\n",
            "51/51 [==============================] - 5s 89ms/step - loss: 809154.4279 - reconstruction_loss: 13371.1396 - vqvae_loss: 795806.1418\n",
            "Epoch 1646/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 802182.4315 - reconstruction_loss: 13438.7344 - vqvae_loss: 788613.1442\n",
            "Epoch 1647/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 791954.5397 - reconstruction_loss: 13305.2197 - vqvae_loss: 778630.4339\n",
            "Epoch 1648/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 797231.2188 - reconstruction_loss: 13351.6582 - vqvae_loss: 783930.9183\n",
            "Epoch 1649/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 804811.1803 - reconstruction_loss: 13350.2324 - vqvae_loss: 791482.9712\n",
            "Epoch 1650/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 786599.5962 - reconstruction_loss: 13339.8076 - vqvae_loss: 773279.0276\n",
            "Epoch 1651/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 803865.6887 - reconstruction_loss: 13339.9473 - vqvae_loss: 790643.4291\n",
            "Epoch 1652/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 794119.6322 - reconstruction_loss: 13393.2363 - vqvae_loss: 780744.5853\n",
            "Epoch 1653/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 801861.6611 - reconstruction_loss: 13348.7158 - vqvae_loss: 788404.6923\n",
            "Epoch 1654/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 800068.0721 - reconstruction_loss: 13343.7500 - vqvae_loss: 786718.4700\n",
            "Epoch 1655/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 790054.6406 - reconstruction_loss: 13328.6523 - vqvae_loss: 776765.0325\n",
            "Epoch 1656/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 796703.1550 - reconstruction_loss: 13331.8838 - vqvae_loss: 783462.6394\n",
            "Epoch 1657/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 801681.4279 - reconstruction_loss: 13395.3232 - vqvae_loss: 788242.5889\n",
            "Epoch 1658/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 801889.4315 - reconstruction_loss: 13409.7881 - vqvae_loss: 788443.1466\n",
            "Epoch 1659/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 802116.9459 - reconstruction_loss: 13410.8789 - vqvae_loss: 788794.6947\n",
            "Epoch 1660/2500\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 795025.2115 - reconstruction_loss: 13405.9746 - vqvae_loss: 781539.2740\n",
            "Epoch 1661/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 802306.1334 - reconstruction_loss: 13384.0732 - vqvae_loss: 788905.2079\n",
            "Epoch 1662/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 800150.0144 - reconstruction_loss: 13368.8701 - vqvae_loss: 786859.0168\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 800150.0144 - reconstruction_loss: 13368.8701 - vqvae_loss: 786859.0168\n",
            "Epoch 1663/2500\n",
            "Epoch 1663/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800190.3630 - reconstruction_loss: 13308.1748 - vqvae_loss: 786919.6635\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800190.3630 - reconstruction_loss: 13308.1748 - vqvae_loss: 786919.6635\n",
            "Epoch 1664/2500\n",
            "Epoch 1664/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 805090.1851 - reconstruction_loss: 13313.0654 - vqvae_loss: 791733.8209\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 805090.1851 - reconstruction_loss: 13313.0654 - vqvae_loss: 791733.8209\n",
            "Epoch 1665/2500\n",
            "Epoch 1665/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 803627.4291 - reconstruction_loss: 13408.0332 - vqvae_loss: 790257.1130\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 803627.4291 - reconstruction_loss: 13408.0332 - vqvae_loss: 790257.1130\n",
            "Epoch 1666/2500\n",
            "Epoch 1666/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 807211.5493 - reconstruction_loss: 13415.9795 - vqvae_loss: 793678.8137\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 807211.5493 - reconstruction_loss: 13415.9795 - vqvae_loss: 793678.8137\n",
            "Epoch 1667/2500\n",
            "Epoch 1667/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 802512.2500 - reconstruction_loss: 13420.4512 - vqvae_loss: 789154.4543\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 802512.2500 - reconstruction_loss: 13420.4512 - vqvae_loss: 789154.4543\n",
            "Epoch 1668/2500\n",
            "Epoch 1668/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 798787.6599 - reconstruction_loss: 13421.4287 - vqvae_loss: 785260.4964\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 798787.6599 - reconstruction_loss: 13421.4287 - vqvae_loss: 785260.4964\n",
            "Epoch 1669/2500\n",
            "Epoch 1669/2500\n",
            "51/51 [==============================] - 5s 101ms/step - loss: 798860.4627 - reconstruction_loss: 13364.5254 - vqvae_loss: 785495.5036\n",
            "51/51 [==============================] - 5s 101ms/step - loss: 798860.4627 - reconstruction_loss: 13364.5254 - vqvae_loss: 785495.5036\n",
            "Epoch 1670/2500\n",
            "Epoch 1670/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 799851.8834 - reconstruction_loss: 13315.0830 - vqvae_loss: 786664.7332\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 799851.8834 - reconstruction_loss: 13315.0830 - vqvae_loss: 786664.7332\n",
            "Epoch 1671/2500\n",
            "Epoch 1671/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 802196.7163 - reconstruction_loss: 13284.4795 - vqvae_loss: 788792.5469\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 802196.7163 - reconstruction_loss: 13284.4795 - vqvae_loss: 788792.5469\n",
            "Epoch 1672/2500\n",
            "Epoch 1672/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 789105.6202 - reconstruction_loss: 13314.5195 - vqvae_loss: 775806.3413\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 789105.6202 - reconstruction_loss: 13314.5195 - vqvae_loss: 775806.3413\n",
            "Epoch 1673/2500\n",
            "Epoch 1673/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 803810.6779 - reconstruction_loss: 13333.6484 - vqvae_loss: 790417.9075\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 803810.6779 - reconstruction_loss: 13333.6484 - vqvae_loss: 790417.9075\n",
            "Epoch 1674/2500\n",
            "Epoch 1674/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 804185.8894 - reconstruction_loss: 13313.6064 - vqvae_loss: 790902.9111\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 804185.8894 - reconstruction_loss: 13313.6064 - vqvae_loss: 790902.9111\n",
            "Epoch 1675/2500\n",
            "Epoch 1675/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808334.9700 - reconstruction_loss: 13371.2129 - vqvae_loss: 794970.2873\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808334.9700 - reconstruction_loss: 13371.2129 - vqvae_loss: 794970.2873\n",
            "Epoch 1676/2500\n",
            "Epoch 1676/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 818523.6154 - reconstruction_loss: 13460.8154 - vqvae_loss: 804904.1166\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 818523.6154 - reconstruction_loss: 13460.8154 - vqvae_loss: 804904.1166\n",
            "Epoch 1677/2500\n",
            "Epoch 1677/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 790308.7524 - reconstruction_loss: 13405.5098 - vqvae_loss: 777035.4495\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 790308.7524 - reconstruction_loss: 13405.5098 - vqvae_loss: 777035.4495\n",
            "Epoch 1678/2500\n",
            "Epoch 1678/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 785978.8570 - reconstruction_loss: 13486.7510 - vqvae_loss: 772395.5565\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 785978.8570 - reconstruction_loss: 13486.7510 - vqvae_loss: 772395.5565\n",
            "Epoch 1679/2500\n",
            "Epoch 1679/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 796874.1466 - reconstruction_loss: 13482.6631 - vqvae_loss: 783417.3329\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 796874.1466 - reconstruction_loss: 13482.6631 - vqvae_loss: 783417.3329\n",
            "Epoch 1680/2500\n",
            "Epoch 1680/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 788407.3029 - reconstruction_loss: 13475.3574 - vqvae_loss: 774985.0637\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 788407.3029 - reconstruction_loss: 13475.3574 - vqvae_loss: 774985.0637\n",
            "Epoch 1681/2500\n",
            "Epoch 1681/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 795573.7668 - reconstruction_loss: 13422.9766 - vqvae_loss: 782134.3474\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 795573.7668 - reconstruction_loss: 13422.9766 - vqvae_loss: 782134.3474\n",
            "Epoch 1682/2500\n",
            "Epoch 1682/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 789315.3822 - reconstruction_loss: 13291.4922 - vqvae_loss: 775885.3918\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 789315.3822 - reconstruction_loss: 13291.4922 - vqvae_loss: 775885.3918\n",
            "Epoch 1683/2500\n",
            "Epoch 1683/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 787077.5974 - reconstruction_loss: 13284.7656 - vqvae_loss: 773728.0048\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 787077.5974 - reconstruction_loss: 13284.7656 - vqvae_loss: 773728.0048\n",
            "Epoch 1684/2500\n",
            "Epoch 1684/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 789931.2151 - reconstruction_loss: 13256.5830 - vqvae_loss: 776644.0805\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 789931.2151 - reconstruction_loss: 13256.5830 - vqvae_loss: 776644.0805\n",
            "Epoch 1685/2500\n",
            "Epoch 1685/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 790901.5637 - reconstruction_loss: 13276.5703 - vqvae_loss: 777474.3990\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 790901.5637 - reconstruction_loss: 13276.5703 - vqvae_loss: 777474.3990\n",
            "Epoch 1686/2500\n",
            "Epoch 1686/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 786122.1022 - reconstruction_loss: 13304.5967 - vqvae_loss: 772787.6490\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 786122.1022 - reconstruction_loss: 13304.5967 - vqvae_loss: 772787.6490\n",
            "Epoch 1687/2500\n",
            "Epoch 1687/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 796963.2692 - reconstruction_loss: 13361.4766 - vqvae_loss: 783551.8642\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 796963.2692 - reconstruction_loss: 13361.4766 - vqvae_loss: 783551.8642\n",
            "Epoch 1688/2500\n",
            "Epoch 1688/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 790819.7500 - reconstruction_loss: 13489.1299 - vqvae_loss: 777244.8017\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 790819.7500 - reconstruction_loss: 13489.1299 - vqvae_loss: 777244.8017\n",
            "Epoch 1689/2500\n",
            "Epoch 1689/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 786723.8726 - reconstruction_loss: 13443.3779 - vqvae_loss: 773260.6046\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 786723.8726 - reconstruction_loss: 13443.3779 - vqvae_loss: 773260.6046\n",
            "Epoch 1690/2500\n",
            "Epoch 1690/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780643.0769 - reconstruction_loss: 13520.5811 - vqvae_loss: 767227.4038\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780643.0769 - reconstruction_loss: 13520.5811 - vqvae_loss: 767227.4038\n",
            "Epoch 1691/2500\n",
            "Epoch 1691/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 782548.6562 - reconstruction_loss: 13572.5088 - vqvae_loss: 768885.6731\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 782548.6562 - reconstruction_loss: 13572.5088 - vqvae_loss: 768885.6731\n",
            "Epoch 1692/2500\n",
            "Epoch 1692/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773469.3089 - reconstruction_loss: 13398.4961 - vqvae_loss: 759978.1394\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773469.3089 - reconstruction_loss: 13398.4961 - vqvae_loss: 759978.1394\n",
            "Epoch 1693/2500\n",
            "Epoch 1693/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 785612.5998 - reconstruction_loss: 13271.1475 - vqvae_loss: 772293.2091\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 785612.5998 - reconstruction_loss: 13271.1475 - vqvae_loss: 772293.2091\n",
            "Epoch 1694/2500\n",
            "Epoch 1694/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 793221.1827 - reconstruction_loss: 13244.2256 - vqvae_loss: 779887.4844\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 793221.1827 - reconstruction_loss: 13244.2256 - vqvae_loss: 779887.4844\n",
            "Epoch 1695/2500\n",
            "Epoch 1695/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 784621.2103 - reconstruction_loss: 13330.6641 - vqvae_loss: 771319.6659\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 784621.2103 - reconstruction_loss: 13330.6641 - vqvae_loss: 771319.6659\n",
            "Epoch 1696/2500\n",
            "Epoch 1696/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 784106.2115 - reconstruction_loss: 13395.2510 - vqvae_loss: 770804.3630\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 784106.2115 - reconstruction_loss: 13395.2510 - vqvae_loss: 770804.3630\n",
            "Epoch 1697/2500\n",
            "Epoch 1697/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 793187.7284 - reconstruction_loss: 13368.1602 - vqvae_loss: 779683.4050\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 793187.7284 - reconstruction_loss: 13368.1602 - vqvae_loss: 779683.4050\n",
            "Epoch 1698/2500\n",
            "Epoch 1698/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 778438.2272 - reconstruction_loss: 13379.7021 - vqvae_loss: 765020.9183\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 778438.2272 - reconstruction_loss: 13379.7021 - vqvae_loss: 765020.9183\n",
            "Epoch 1699/2500\n",
            "Epoch 1699/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 796310.9916 - reconstruction_loss: 13480.5137 - vqvae_loss: 782849.3582\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 796310.9916 - reconstruction_loss: 13480.5137 - vqvae_loss: 782849.3582\n",
            "Epoch 1700/2500\n",
            "Epoch 1700/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 779886.7115 - reconstruction_loss: 13615.0537 - vqvae_loss: 766322.9471\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 779886.7115 - reconstruction_loss: 13615.0537 - vqvae_loss: 766322.9471\n",
            "Epoch 1701/2500\n",
            "Epoch 1701/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 777110.6370 - reconstruction_loss: 13466.5703 - vqvae_loss: 763647.6875\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 777110.6370 - reconstruction_loss: 13466.5703 - vqvae_loss: 763647.6875\n",
            "Epoch 1702/2500\n",
            "Epoch 1702/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 780687.6370 - reconstruction_loss: 13475.0273 - vqvae_loss: 767252.1250\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 780687.6370 - reconstruction_loss: 13475.0273 - vqvae_loss: 767252.1250\n",
            "Epoch 1703/2500\n",
            "Epoch 1703/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 784143.5096 - reconstruction_loss: 13351.1738 - vqvae_loss: 770748.3714\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 784143.5096 - reconstruction_loss: 13351.1738 - vqvae_loss: 770748.3714\n",
            "Epoch 1704/2500\n",
            "Epoch 1704/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775550.0601 - reconstruction_loss: 13311.1436 - vqvae_loss: 762223.5421\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775550.0601 - reconstruction_loss: 13311.1436 - vqvae_loss: 762223.5421\n",
            "Epoch 1705/2500\n",
            "Epoch 1705/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 774235.8101 - reconstruction_loss: 13479.8828 - vqvae_loss: 760861.5024\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 774235.8101 - reconstruction_loss: 13479.8828 - vqvae_loss: 760861.5024\n",
            "Epoch 1706/2500\n",
            "Epoch 1706/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 774109.3257 - reconstruction_loss: 13518.1338 - vqvae_loss: 760620.3462\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 774109.3257 - reconstruction_loss: 13518.1338 - vqvae_loss: 760620.3462\n",
            "Epoch 1707/2500\n",
            "Epoch 1707/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 771507.1635 - reconstruction_loss: 13429.2822 - vqvae_loss: 758102.5697\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 771507.1635 - reconstruction_loss: 13429.2822 - vqvae_loss: 758102.5697\n",
            "Epoch 1708/2500\n",
            "Epoch 1708/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 771794.7800 - reconstruction_loss: 13391.5850 - vqvae_loss: 758500.1983\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 771794.7800 - reconstruction_loss: 13391.5850 - vqvae_loss: 758500.1983\n",
            "Epoch 1709/2500\n",
            "Epoch 1709/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780755.4688 - reconstruction_loss: 13363.4697 - vqvae_loss: 767312.8161\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780755.4688 - reconstruction_loss: 13363.4697 - vqvae_loss: 767312.8161\n",
            "Epoch 1710/2500\n",
            "Epoch 1710/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 775159.8329 - reconstruction_loss: 13412.1104 - vqvae_loss: 761946.6779\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 775159.8329 - reconstruction_loss: 13412.1104 - vqvae_loss: 761946.6779\n",
            "Epoch 1711/2500\n",
            "Epoch 1711/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 779942.4615 - reconstruction_loss: 13391.2002 - vqvae_loss: 766584.9688\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 779942.4615 - reconstruction_loss: 13391.2002 - vqvae_loss: 766584.9688\n",
            "Epoch 1712/2500\n",
            "Epoch 1712/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 775459.6514 - reconstruction_loss: 13486.5107 - vqvae_loss: 761896.9928\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 775459.6514 - reconstruction_loss: 13486.5107 - vqvae_loss: 761896.9928\n",
            "Epoch 1713/2500\n",
            "Epoch 1713/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 776409.8918 - reconstruction_loss: 13478.4170 - vqvae_loss: 762907.6346\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 776409.8918 - reconstruction_loss: 13478.4170 - vqvae_loss: 762907.6346\n",
            "Epoch 1714/2500\n",
            "Epoch 1714/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 772999.3906 - reconstruction_loss: 13458.9004 - vqvae_loss: 759580.1010\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 772999.3906 - reconstruction_loss: 13458.9004 - vqvae_loss: 759580.1010\n",
            "Epoch 1715/2500\n",
            "Epoch 1715/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 773092.4748 - reconstruction_loss: 13606.6689 - vqvae_loss: 759500.2043\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 773092.4748 - reconstruction_loss: 13606.6689 - vqvae_loss: 759500.2043\n",
            "Epoch 1716/2500\n",
            "Epoch 1716/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 767395.4147 - reconstruction_loss: 13476.1221 - vqvae_loss: 754073.9651\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 767395.4147 - reconstruction_loss: 13476.1221 - vqvae_loss: 754073.9651\n",
            "Epoch 1717/2500\n",
            "Epoch 1717/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768272.4459 - reconstruction_loss: 13507.3076 - vqvae_loss: 754784.9796\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768272.4459 - reconstruction_loss: 13507.3076 - vqvae_loss: 754784.9796\n",
            "Epoch 1718/2500\n",
            "Epoch 1718/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 774430.2103 - reconstruction_loss: 13464.7266 - vqvae_loss: 760969.6887\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 774430.2103 - reconstruction_loss: 13464.7266 - vqvae_loss: 760969.6887\n",
            "Epoch 1719/2500\n",
            "Epoch 1719/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 767883.8065 - reconstruction_loss: 13435.5273 - vqvae_loss: 754572.7055\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 767883.8065 - reconstruction_loss: 13435.5273 - vqvae_loss: 754572.7055\n",
            "Epoch 1720/2500\n",
            "Epoch 1720/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 789516.1707 - reconstruction_loss: 13391.7168 - vqvae_loss: 776083.6767\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 789516.1707 - reconstruction_loss: 13391.7168 - vqvae_loss: 776083.6767\n",
            "Epoch 1721/2500\n",
            "Epoch 1721/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773877.1599 - reconstruction_loss: 13331.4277 - vqvae_loss: 760422.4075\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773877.1599 - reconstruction_loss: 13331.4277 - vqvae_loss: 760422.4075\n",
            "Epoch 1722/2500\n",
            "Epoch 1722/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772512.2728 - reconstruction_loss: 13411.5850 - vqvae_loss: 759173.1190\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772512.2728 - reconstruction_loss: 13411.5850 - vqvae_loss: 759173.1190\n",
            "Epoch 1723/2500\n",
            "Epoch 1723/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 776798.9603 - reconstruction_loss: 13481.6953 - vqvae_loss: 763402.8486\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 776798.9603 - reconstruction_loss: 13481.6953 - vqvae_loss: 763402.8486\n",
            "Epoch 1724/2500\n",
            "Epoch 1724/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780466.8690 - reconstruction_loss: 13583.5732 - vqvae_loss: 766897.6719\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780466.8690 - reconstruction_loss: 13583.5732 - vqvae_loss: 766897.6719\n",
            "Epoch 1725/2500\n",
            "Epoch 1725/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768247.3678 - reconstruction_loss: 13635.6953 - vqvae_loss: 754613.1238\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768247.3678 - reconstruction_loss: 13635.6953 - vqvae_loss: 754613.1238\n",
            "Epoch 1726/2500\n",
            "Epoch 1726/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771629.1562 - reconstruction_loss: 13620.2363 - vqvae_loss: 757911.3990\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771629.1562 - reconstruction_loss: 13620.2363 - vqvae_loss: 757911.3990\n",
            "Epoch 1727/2500\n",
            "Epoch 1727/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 770318.1214 - reconstruction_loss: 13689.0420 - vqvae_loss: 756663.2488\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 770318.1214 - reconstruction_loss: 13689.0420 - vqvae_loss: 756663.2488\n",
            "Epoch 1728/2500\n",
            "Epoch 1728/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 775711.7151 - reconstruction_loss: 13644.8203 - vqvae_loss: 762054.7752\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 775711.7151 - reconstruction_loss: 13644.8203 - vqvae_loss: 762054.7752\n",
            "Epoch 1729/2500\n",
            "Epoch 1729/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 781684.5841 - reconstruction_loss: 13701.9004 - vqvae_loss: 767898.1923\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 781684.5841 - reconstruction_loss: 13701.9004 - vqvae_loss: 767898.1923\n",
            "Epoch 1730/2500\n",
            "Epoch 1730/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 775928.9820 - reconstruction_loss: 13659.7178 - vqvae_loss: 762287.1815\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 775928.9820 - reconstruction_loss: 13659.7178 - vqvae_loss: 762287.1815\n",
            "Epoch 1731/2500\n",
            "Epoch 1731/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 785763.4014 - reconstruction_loss: 13608.6074 - vqvae_loss: 772206.8894\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 785763.4014 - reconstruction_loss: 13608.6074 - vqvae_loss: 772206.8894\n",
            "Epoch 1732/2500\n",
            "Epoch 1732/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 773580.2031 - reconstruction_loss: 13551.9248 - vqvae_loss: 760123.1478\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 773580.2031 - reconstruction_loss: 13551.9248 - vqvae_loss: 760123.1478\n",
            "Epoch 1733/2500\n",
            "Epoch 1733/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 787744.9062 - reconstruction_loss: 13513.9102 - vqvae_loss: 774112.0288\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 787744.9062 - reconstruction_loss: 13513.9102 - vqvae_loss: 774112.0288\n",
            "Epoch 1734/2500\n",
            "Epoch 1734/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 788346.7464 - reconstruction_loss: 13599.6953 - vqvae_loss: 774841.0649\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 788346.7464 - reconstruction_loss: 13599.6953 - vqvae_loss: 774841.0649\n",
            "Epoch 1735/2500\n",
            "Epoch 1735/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780402.3834 - reconstruction_loss: 13566.8086 - vqvae_loss: 766758.5721\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780402.3834 - reconstruction_loss: 13566.8086 - vqvae_loss: 766758.5721\n",
            "Epoch 1736/2500\n",
            "Epoch 1736/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 794639.1683 - reconstruction_loss: 13673.1738 - vqvae_loss: 780980.6707\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 794639.1683 - reconstruction_loss: 13673.1738 - vqvae_loss: 780980.6707\n",
            "Epoch 1737/2500\n",
            "Epoch 1737/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 795496.7608 - reconstruction_loss: 13566.3418 - vqvae_loss: 781994.0168\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 795496.7608 - reconstruction_loss: 13566.3418 - vqvae_loss: 781994.0168\n",
            "Epoch 1738/2500\n",
            "Epoch 1738/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 795618.4856 - reconstruction_loss: 13690.4580 - vqvae_loss: 781998.8570\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 795618.4856 - reconstruction_loss: 13690.4580 - vqvae_loss: 781998.8570\n",
            "Epoch 1739/2500\n",
            "Epoch 1739/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 787838.0601 - reconstruction_loss: 13723.0127 - vqvae_loss: 774215.3678\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 787838.0601 - reconstruction_loss: 13723.0127 - vqvae_loss: 774215.3678\n",
            "Epoch 1740/2500\n",
            "Epoch 1740/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 791648.3474 - reconstruction_loss: 13680.8350 - vqvae_loss: 777904.2428\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 791648.3474 - reconstruction_loss: 13680.8350 - vqvae_loss: 777904.2428\n",
            "Epoch 1741/2500\n",
            "Epoch 1741/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 788982.8293 - reconstruction_loss: 13683.4385 - vqvae_loss: 775290.1334\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 788982.8293 - reconstruction_loss: 13683.4385 - vqvae_loss: 775290.1334\n",
            "Epoch 1742/2500\n",
            "Epoch 1742/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800134.6695 - reconstruction_loss: 13706.1396 - vqvae_loss: 786506.3606\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800134.6695 - reconstruction_loss: 13706.1396 - vqvae_loss: 786506.3606\n",
            "Epoch 1743/2500\n",
            "Epoch 1743/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 791716.2608 - reconstruction_loss: 13660.1738 - vqvae_loss: 778121.1971\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 791716.2608 - reconstruction_loss: 13660.1738 - vqvae_loss: 778121.1971\n",
            "Epoch 1744/2500\n",
            "Epoch 1744/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 806074.5805 - reconstruction_loss: 13644.2227 - vqvae_loss: 792348.4579\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 806074.5805 - reconstruction_loss: 13644.2227 - vqvae_loss: 792348.4579\n",
            "Epoch 1745/2500\n",
            "Epoch 1745/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812021.6695 - reconstruction_loss: 13691.5303 - vqvae_loss: 798289.3618\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812021.6695 - reconstruction_loss: 13691.5303 - vqvae_loss: 798289.3618\n",
            "Epoch 1746/2500\n",
            "Epoch 1746/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815193.6683 - reconstruction_loss: 13640.8369 - vqvae_loss: 801544.3029\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815193.6683 - reconstruction_loss: 13640.8369 - vqvae_loss: 801544.3029\n",
            "Epoch 1747/2500\n",
            "Epoch 1747/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 814247.8474 - reconstruction_loss: 13693.3428 - vqvae_loss: 800494.1382\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 814247.8474 - reconstruction_loss: 13693.3428 - vqvae_loss: 800494.1382\n",
            "Epoch 1748/2500\n",
            "Epoch 1748/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808759.8510 - reconstruction_loss: 13647.4434 - vqvae_loss: 795190.1526\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808759.8510 - reconstruction_loss: 13647.4434 - vqvae_loss: 795190.1526\n",
            "Epoch 1749/2500\n",
            "Epoch 1749/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 823930.9832 - reconstruction_loss: 13676.3604 - vqvae_loss: 810271.2139\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 823930.9832 - reconstruction_loss: 13676.3604 - vqvae_loss: 810271.2139\n",
            "Epoch 1750/2500\n",
            "Epoch 1750/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823664.7740 - reconstruction_loss: 13710.6221 - vqvae_loss: 809848.2957\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823664.7740 - reconstruction_loss: 13710.6221 - vqvae_loss: 809848.2957\n",
            "Epoch 1751/2500\n",
            "Epoch 1751/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 820418.4916 - reconstruction_loss: 13691.4053 - vqvae_loss: 806762.0397\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 820418.4916 - reconstruction_loss: 13691.4053 - vqvae_loss: 806762.0397\n",
            "Epoch 1752/2500\n",
            "Epoch 1752/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 822048.1190 - reconstruction_loss: 13672.4238 - vqvae_loss: 808352.4988\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 822048.1190 - reconstruction_loss: 13672.4238 - vqvae_loss: 808352.4988\n",
            "Epoch 1753/2500\n",
            "Epoch 1753/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826974.9724 - reconstruction_loss: 13649.2588 - vqvae_loss: 813337.9868\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826974.9724 - reconstruction_loss: 13649.2588 - vqvae_loss: 813337.9868\n",
            "Epoch 1754/2500\n",
            "Epoch 1754/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 833186.1875 - reconstruction_loss: 13713.7676 - vqvae_loss: 819538.5481\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 833186.1875 - reconstruction_loss: 13713.7676 - vqvae_loss: 819538.5481\n",
            "Epoch 1755/2500\n",
            "Epoch 1755/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 835973.2007 - reconstruction_loss: 13678.0039 - vqvae_loss: 822379.7500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 835973.2007 - reconstruction_loss: 13678.0039 - vqvae_loss: 822379.7500\n",
            "Epoch 1756/2500\n",
            "Epoch 1756/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 837611.0072 - reconstruction_loss: 13678.3789 - vqvae_loss: 823975.5733\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 837611.0072 - reconstruction_loss: 13678.3789 - vqvae_loss: 823975.5733\n",
            "Epoch 1757/2500\n",
            "Epoch 1757/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 842406.0625 - reconstruction_loss: 13622.4775 - vqvae_loss: 828806.9399\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 842406.0625 - reconstruction_loss: 13622.4775 - vqvae_loss: 828806.9399\n",
            "Epoch 1758/2500\n",
            "Epoch 1758/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 835314.7248 - reconstruction_loss: 13638.7559 - vqvae_loss: 821757.1394\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 835314.7248 - reconstruction_loss: 13638.7559 - vqvae_loss: 821757.1394\n",
            "Epoch 1759/2500\n",
            "Epoch 1759/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 845553.7272 - reconstruction_loss: 13660.0059 - vqvae_loss: 831968.7200\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 845553.7272 - reconstruction_loss: 13660.0059 - vqvae_loss: 831968.7200\n",
            "Epoch 1760/2500\n",
            "Epoch 1760/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 830381.2644 - reconstruction_loss: 13620.5488 - vqvae_loss: 816665.4916\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 830381.2644 - reconstruction_loss: 13620.5488 - vqvae_loss: 816665.4916\n",
            "Epoch 1761/2500\n",
            "Epoch 1761/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 838200.6310 - reconstruction_loss: 13610.9531 - vqvae_loss: 824647.0709\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 838200.6310 - reconstruction_loss: 13610.9531 - vqvae_loss: 824647.0709\n",
            "Epoch 1762/2500\n",
            "Epoch 1762/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 835940.4147 - reconstruction_loss: 13616.4189 - vqvae_loss: 822256.2656\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 835940.4147 - reconstruction_loss: 13616.4189 - vqvae_loss: 822256.2656\n",
            "Epoch 1763/2500\n",
            "Epoch 1763/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 829098.8005 - reconstruction_loss: 13614.6914 - vqvae_loss: 815436.6298\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 829098.8005 - reconstruction_loss: 13614.6914 - vqvae_loss: 815436.6298\n",
            "Epoch 1764/2500\n",
            "Epoch 1764/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824865.4219 - reconstruction_loss: 13623.2969 - vqvae_loss: 811279.6635\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824865.4219 - reconstruction_loss: 13623.2969 - vqvae_loss: 811279.6635\n",
            "Epoch 1765/2500\n",
            "Epoch 1765/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 821875.5721 - reconstruction_loss: 13636.8936 - vqvae_loss: 808221.1526\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 821875.5721 - reconstruction_loss: 13636.8936 - vqvae_loss: 808221.1526\n",
            "Epoch 1766/2500\n",
            "Epoch 1766/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826579.7200 - reconstruction_loss: 13611.9854 - vqvae_loss: 812975.5385\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826579.7200 - reconstruction_loss: 13611.9854 - vqvae_loss: 812975.5385\n",
            "Epoch 1767/2500\n",
            "Epoch 1767/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 815822.7812 - reconstruction_loss: 13606.2725 - vqvae_loss: 802204.6683\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 815822.7812 - reconstruction_loss: 13606.2725 - vqvae_loss: 802204.6683\n",
            "Epoch 1768/2500\n",
            "Epoch 1768/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 822730.5829 - reconstruction_loss: 13595.5469 - vqvae_loss: 809160.3101\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 822730.5829 - reconstruction_loss: 13595.5469 - vqvae_loss: 809160.3101\n",
            "Epoch 1769/2500\n",
            "Epoch 1769/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 824595.7752 - reconstruction_loss: 13627.3506 - vqvae_loss: 810971.4147\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 824595.7752 - reconstruction_loss: 13627.3506 - vqvae_loss: 810971.4147\n",
            "Epoch 1770/2500\n",
            "Epoch 1770/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 828640.0685 - reconstruction_loss: 13575.0303 - vqvae_loss: 814975.1683\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 828640.0685 - reconstruction_loss: 13575.0303 - vqvae_loss: 814975.1683\n",
            "Epoch 1771/2500\n",
            "Epoch 1771/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 818963.3329 - reconstruction_loss: 13610.9707 - vqvae_loss: 805369.0048\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 818963.3329 - reconstruction_loss: 13610.9707 - vqvae_loss: 805369.0048\n",
            "Epoch 1772/2500\n",
            "Epoch 1772/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 818818.1827 - reconstruction_loss: 13565.8594 - vqvae_loss: 805132.7548\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 818818.1827 - reconstruction_loss: 13565.8594 - vqvae_loss: 805132.7548\n",
            "Epoch 1773/2500\n",
            "Epoch 1773/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 824758.7825 - reconstruction_loss: 13546.1279 - vqvae_loss: 811298.0517\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 824758.7825 - reconstruction_loss: 13546.1279 - vqvae_loss: 811298.0517\n",
            "Epoch 1774/2500\n",
            "Epoch 1774/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815194.2812 - reconstruction_loss: 13546.4082 - vqvae_loss: 801726.7091\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815194.2812 - reconstruction_loss: 13546.4082 - vqvae_loss: 801726.7091\n",
            "Epoch 1775/2500\n",
            "Epoch 1775/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 833790.0841 - reconstruction_loss: 13517.4814 - vqvae_loss: 820181.2055\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 833790.0841 - reconstruction_loss: 13517.4814 - vqvae_loss: 820181.2055\n",
            "Epoch 1776/2500\n",
            "Epoch 1776/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 835215.8425 - reconstruction_loss: 13538.4629 - vqvae_loss: 821647.0120\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 835215.8425 - reconstruction_loss: 13538.4629 - vqvae_loss: 821647.0120\n",
            "Epoch 1777/2500\n",
            "Epoch 1777/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 819399.2945 - reconstruction_loss: 13561.7900 - vqvae_loss: 805772.0733\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 819399.2945 - reconstruction_loss: 13561.7900 - vqvae_loss: 805772.0733\n",
            "Epoch 1778/2500\n",
            "Epoch 1778/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 827356.1034 - reconstruction_loss: 13559.0293 - vqvae_loss: 813750.7800\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 827356.1034 - reconstruction_loss: 13559.0293 - vqvae_loss: 813750.7800\n",
            "Epoch 1779/2500\n",
            "Epoch 1779/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823648.3882 - reconstruction_loss: 13552.2178 - vqvae_loss: 810146.2200\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823648.3882 - reconstruction_loss: 13552.2178 - vqvae_loss: 810146.2200\n",
            "Epoch 1780/2500\n",
            "Epoch 1780/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824023.1202 - reconstruction_loss: 13512.6191 - vqvae_loss: 810591.9159\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824023.1202 - reconstruction_loss: 13512.6191 - vqvae_loss: 810591.9159\n",
            "Epoch 1781/2500\n",
            "Epoch 1781/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 816678.0252 - reconstruction_loss: 13504.1396 - vqvae_loss: 803090.4014\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 816678.0252 - reconstruction_loss: 13504.1396 - vqvae_loss: 803090.4014\n",
            "Epoch 1782/2500\n",
            "Epoch 1782/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 811733.6418 - reconstruction_loss: 13511.4131 - vqvae_loss: 798263.0409\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 811733.6418 - reconstruction_loss: 13511.4131 - vqvae_loss: 798263.0409\n",
            "Epoch 1783/2500\n",
            "Epoch 1783/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 809583.7524 - reconstruction_loss: 13522.8135 - vqvae_loss: 795989.1454\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 809583.7524 - reconstruction_loss: 13522.8135 - vqvae_loss: 795989.1454\n",
            "Epoch 1784/2500\n",
            "Epoch 1784/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 806119.0577 - reconstruction_loss: 13502.0635 - vqvae_loss: 792645.7368\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 806119.0577 - reconstruction_loss: 13502.0635 - vqvae_loss: 792645.7368\n",
            "Epoch 1785/2500\n",
            "Epoch 1785/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 806110.7740 - reconstruction_loss: 13510.9727 - vqvae_loss: 792531.0024\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 806110.7740 - reconstruction_loss: 13510.9727 - vqvae_loss: 792531.0024\n",
            "Epoch 1786/2500\n",
            "Epoch 1786/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 805552.4038 - reconstruction_loss: 13495.8643 - vqvae_loss: 791930.5529\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 805552.4038 - reconstruction_loss: 13495.8643 - vqvae_loss: 791930.5529\n",
            "Epoch 1787/2500\n",
            "Epoch 1787/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 809973.1190 - reconstruction_loss: 13447.9316 - vqvae_loss: 796564.5637\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 809973.1190 - reconstruction_loss: 13447.9316 - vqvae_loss: 796564.5637\n",
            "Epoch 1788/2500\n",
            "Epoch 1788/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 819031.1370 - reconstruction_loss: 13449.0449 - vqvae_loss: 805515.5553\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 819031.1370 - reconstruction_loss: 13449.0449 - vqvae_loss: 805515.5553\n",
            "Epoch 1789/2500\n",
            "Epoch 1789/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 818358.8654 - reconstruction_loss: 13452.9375 - vqvae_loss: 804892.4327\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 818358.8654 - reconstruction_loss: 13452.9375 - vqvae_loss: 804892.4327\n",
            "Epoch 1790/2500\n",
            "Epoch 1790/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 810848.3870 - reconstruction_loss: 13450.4482 - vqvae_loss: 797436.1887\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 810848.3870 - reconstruction_loss: 13450.4482 - vqvae_loss: 797436.1887\n",
            "Epoch 1791/2500\n",
            "Epoch 1791/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 814310.3954 - reconstruction_loss: 13456.1309 - vqvae_loss: 800836.7260\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 814310.3954 - reconstruction_loss: 13456.1309 - vqvae_loss: 800836.7260\n",
            "Epoch 1792/2500\n",
            "Epoch 1792/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 811326.8630 - reconstruction_loss: 13474.9502 - vqvae_loss: 797963.7368\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 811326.8630 - reconstruction_loss: 13474.9502 - vqvae_loss: 797963.7368\n",
            "Epoch 1793/2500\n",
            "Epoch 1793/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 807508.7788 - reconstruction_loss: 13470.5674 - vqvae_loss: 794063.6094\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 807508.7788 - reconstruction_loss: 13470.5674 - vqvae_loss: 794063.6094\n",
            "Epoch 1794/2500\n",
            "Epoch 1794/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 807548.6839 - reconstruction_loss: 13483.6338 - vqvae_loss: 794123.4002\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 807548.6839 - reconstruction_loss: 13483.6338 - vqvae_loss: 794123.4002\n",
            "Epoch 1795/2500\n",
            "Epoch 1795/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 804286.7788 - reconstruction_loss: 13470.7930 - vqvae_loss: 790835.1611\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 804286.7788 - reconstruction_loss: 13470.7930 - vqvae_loss: 790835.1611\n",
            "Epoch 1796/2500\n",
            "Epoch 1796/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819409.9375 - reconstruction_loss: 13523.1748 - vqvae_loss: 805967.4111\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819409.9375 - reconstruction_loss: 13523.1748 - vqvae_loss: 805967.4111\n",
            "Epoch 1797/2500\n",
            "Epoch 1797/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 816117.1334 - reconstruction_loss: 13544.8994 - vqvae_loss: 802595.3185\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 816117.1334 - reconstruction_loss: 13544.8994 - vqvae_loss: 802595.3185\n",
            "Epoch 1798/2500\n",
            "Epoch 1798/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 821431.5084 - reconstruction_loss: 13457.1680 - vqvae_loss: 807920.1611\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 821431.5084 - reconstruction_loss: 13457.1680 - vqvae_loss: 807920.1611\n",
            "Epoch 1799/2500\n",
            "Epoch 1799/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 805245.1562 - reconstruction_loss: 13451.9004 - vqvae_loss: 791749.4111\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 805245.1562 - reconstruction_loss: 13451.9004 - vqvae_loss: 791749.4111\n",
            "Epoch 1800/2500\n",
            "Epoch 1800/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 825627.3029 - reconstruction_loss: 13414.3760 - vqvae_loss: 812235.5817\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 825627.3029 - reconstruction_loss: 13414.3760 - vqvae_loss: 812235.5817\n",
            "Epoch 1801/2500\n",
            "Epoch 1801/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 812002.3137 - reconstruction_loss: 13403.9893 - vqvae_loss: 798662.3954\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 812002.3137 - reconstruction_loss: 13403.9893 - vqvae_loss: 798662.3954\n",
            "Epoch 1802/2500\n",
            "Epoch 1802/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 820940.1514 - reconstruction_loss: 13466.9229 - vqvae_loss: 807473.4002\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 820940.1514 - reconstruction_loss: 13466.9229 - vqvae_loss: 807473.4002\n",
            "Epoch 1803/2500\n",
            "Epoch 1803/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 801163.3642 - reconstruction_loss: 13442.4414 - vqvae_loss: 787631.9387\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 801163.3642 - reconstruction_loss: 13442.4414 - vqvae_loss: 787631.9387\n",
            "Epoch 1804/2500\n",
            "Epoch 1804/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 809275.0433 - reconstruction_loss: 13436.6602 - vqvae_loss: 795835.8305\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 809275.0433 - reconstruction_loss: 13436.6602 - vqvae_loss: 795835.8305\n",
            "Epoch 1805/2500\n",
            "Epoch 1805/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 811759.4315 - reconstruction_loss: 13451.2793 - vqvae_loss: 798290.9303\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 811759.4315 - reconstruction_loss: 13451.2793 - vqvae_loss: 798290.9303\n",
            "Epoch 1806/2500\n",
            "Epoch 1806/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 813489.0817 - reconstruction_loss: 13406.1543 - vqvae_loss: 800120.1719\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 813489.0817 - reconstruction_loss: 13406.1543 - vqvae_loss: 800120.1719\n",
            "Epoch 1807/2500\n",
            "Epoch 1807/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 813248.1899 - reconstruction_loss: 13423.5000 - vqvae_loss: 799805.7296\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 813248.1899 - reconstruction_loss: 13423.5000 - vqvae_loss: 799805.7296\n",
            "Epoch 1808/2500\n",
            "Epoch 1808/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 816480.3546 - reconstruction_loss: 13380.4873 - vqvae_loss: 802991.6995\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 816480.3546 - reconstruction_loss: 13380.4873 - vqvae_loss: 802991.6995\n",
            "Epoch 1809/2500\n",
            "Epoch 1809/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826971.3209 - reconstruction_loss: 13369.7510 - vqvae_loss: 813608.7416\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826971.3209 - reconstruction_loss: 13369.7510 - vqvae_loss: 813608.7416\n",
            "Epoch 1810/2500\n",
            "Epoch 1810/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 819354.1514 - reconstruction_loss: 13445.5303 - vqvae_loss: 805881.6394\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 819354.1514 - reconstruction_loss: 13445.5303 - vqvae_loss: 805881.6394\n",
            "Epoch 1811/2500\n",
            "Epoch 1811/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 811237.5829 - reconstruction_loss: 13401.7070 - vqvae_loss: 797777.9147\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 811237.5829 - reconstruction_loss: 13401.7070 - vqvae_loss: 797777.9147\n",
            "Epoch 1812/2500\n",
            "Epoch 1812/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 817276.0601 - reconstruction_loss: 13424.1816 - vqvae_loss: 803790.1550\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 817276.0601 - reconstruction_loss: 13424.1816 - vqvae_loss: 803790.1550\n",
            "Epoch 1813/2500\n",
            "Epoch 1813/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 807735.5685 - reconstruction_loss: 13440.7793 - vqvae_loss: 794325.8041\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 807735.5685 - reconstruction_loss: 13440.7793 - vqvae_loss: 794325.8041\n",
            "Epoch 1814/2500\n",
            "Epoch 1814/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 817362.2548 - reconstruction_loss: 13468.2510 - vqvae_loss: 803897.8630\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 817362.2548 - reconstruction_loss: 13468.2510 - vqvae_loss: 803897.8630\n",
            "Epoch 1815/2500\n",
            "Epoch 1815/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 806908.7055 - reconstruction_loss: 13478.2422 - vqvae_loss: 793328.9868\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 806908.7055 - reconstruction_loss: 13478.2422 - vqvae_loss: 793328.9868\n",
            "Epoch 1816/2500\n",
            "Epoch 1816/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 808315.2488 - reconstruction_loss: 13447.1914 - vqvae_loss: 794848.0036\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 808315.2488 - reconstruction_loss: 13447.1914 - vqvae_loss: 794848.0036\n",
            "Epoch 1817/2500\n",
            "Epoch 1817/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 818818.1310 - reconstruction_loss: 13492.0303 - vqvae_loss: 805391.1190\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 818818.1310 - reconstruction_loss: 13492.0303 - vqvae_loss: 805391.1190\n",
            "Epoch 1818/2500\n",
            "Epoch 1818/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815889.2716 - reconstruction_loss: 13467.7695 - vqvae_loss: 802438.3810\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 815889.2716 - reconstruction_loss: 13467.7695 - vqvae_loss: 802438.3810\n",
            "Epoch 1819/2500\n",
            "Epoch 1819/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 804174.3990 - reconstruction_loss: 13459.9365 - vqvae_loss: 790724.8053\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 804174.3990 - reconstruction_loss: 13459.9365 - vqvae_loss: 790724.8053\n",
            "Epoch 1820/2500\n",
            "Epoch 1820/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 794972.1370 - reconstruction_loss: 13441.8477 - vqvae_loss: 781580.5685\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 794972.1370 - reconstruction_loss: 13441.8477 - vqvae_loss: 781580.5685\n",
            "Epoch 1821/2500\n",
            "Epoch 1821/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812922.4471 - reconstruction_loss: 13452.9238 - vqvae_loss: 799481.6130\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812922.4471 - reconstruction_loss: 13452.9238 - vqvae_loss: 799481.6130\n",
            "Epoch 1822/2500\n",
            "Epoch 1822/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819089.2764 - reconstruction_loss: 13505.2363 - vqvae_loss: 805639.1178\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819089.2764 - reconstruction_loss: 13505.2363 - vqvae_loss: 805639.1178\n",
            "Epoch 1823/2500\n",
            "Epoch 1823/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 820991.6118 - reconstruction_loss: 13427.8369 - vqvae_loss: 807538.0601\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 820991.6118 - reconstruction_loss: 13427.8369 - vqvae_loss: 807538.0601\n",
            "Epoch 1824/2500\n",
            "Epoch 1824/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 813769.0805 - reconstruction_loss: 13465.0488 - vqvae_loss: 800223.4736\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 813769.0805 - reconstruction_loss: 13465.0488 - vqvae_loss: 800223.4736\n",
            "Epoch 1825/2500\n",
            "Epoch 1825/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819556.3281 - reconstruction_loss: 13468.3359 - vqvae_loss: 806135.2680\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819556.3281 - reconstruction_loss: 13468.3359 - vqvae_loss: 806135.2680\n",
            "Epoch 1826/2500\n",
            "Epoch 1826/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 814582.1647 - reconstruction_loss: 13456.9004 - vqvae_loss: 801297.0817\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 814582.1647 - reconstruction_loss: 13456.9004 - vqvae_loss: 801297.0817\n",
            "Epoch 1827/2500\n",
            "Epoch 1827/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 820416.4832 - reconstruction_loss: 13484.0664 - vqvae_loss: 806929.4603\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 820416.4832 - reconstruction_loss: 13484.0664 - vqvae_loss: 806929.4603\n",
            "Epoch 1828/2500\n",
            "Epoch 1828/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 813788.8462 - reconstruction_loss: 13422.2803 - vqvae_loss: 800296.4651\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 813788.8462 - reconstruction_loss: 13422.2803 - vqvae_loss: 800296.4651\n",
            "Epoch 1829/2500\n",
            "Epoch 1829/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 808774.6983 - reconstruction_loss: 13377.0537 - vqvae_loss: 795333.5060\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 808774.6983 - reconstruction_loss: 13377.0537 - vqvae_loss: 795333.5060\n",
            "Epoch 1830/2500\n",
            "Epoch 1830/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 806114.0877 - reconstruction_loss: 13451.9473 - vqvae_loss: 792794.5445\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 806114.0877 - reconstruction_loss: 13451.9473 - vqvae_loss: 792794.5445\n",
            "Epoch 1831/2500\n",
            "Epoch 1831/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 814057.5433 - reconstruction_loss: 13424.1055 - vqvae_loss: 800756.4928\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 814057.5433 - reconstruction_loss: 13424.1055 - vqvae_loss: 800756.4928\n",
            "Epoch 1832/2500\n",
            "Epoch 1832/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819300.2644 - reconstruction_loss: 13453.4697 - vqvae_loss: 805839.4026\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819300.2644 - reconstruction_loss: 13453.4697 - vqvae_loss: 805839.4026\n",
            "Epoch 1833/2500\n",
            "Epoch 1833/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823456.1587 - reconstruction_loss: 13475.3857 - vqvae_loss: 810016.5409\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823456.1587 - reconstruction_loss: 13475.3857 - vqvae_loss: 810016.5409\n",
            "Epoch 1834/2500\n",
            "Epoch 1834/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826504.8089 - reconstruction_loss: 13505.0957 - vqvae_loss: 812854.7572\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826504.8089 - reconstruction_loss: 13505.0957 - vqvae_loss: 812854.7572\n",
            "Epoch 1835/2500\n",
            "Epoch 1835/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 820327.6142 - reconstruction_loss: 13446.1055 - vqvae_loss: 806780.5108\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 820327.6142 - reconstruction_loss: 13446.1055 - vqvae_loss: 806780.5108\n",
            "Epoch 1836/2500\n",
            "Epoch 1836/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 813103.3666 - reconstruction_loss: 13463.9033 - vqvae_loss: 799784.7584\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 813103.3666 - reconstruction_loss: 13463.9033 - vqvae_loss: 799784.7584\n",
            "Epoch 1837/2500\n",
            "Epoch 1837/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 817074.0613 - reconstruction_loss: 13502.9170 - vqvae_loss: 803597.2704\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 817074.0613 - reconstruction_loss: 13502.9170 - vqvae_loss: 803597.2704\n",
            "Epoch 1838/2500\n",
            "Epoch 1838/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 813504.3474 - reconstruction_loss: 13477.1934 - vqvae_loss: 800105.2969\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 813504.3474 - reconstruction_loss: 13477.1934 - vqvae_loss: 800105.2969\n",
            "Epoch 1839/2500\n",
            "Epoch 1839/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 813444.0240 - reconstruction_loss: 13543.8721 - vqvae_loss: 799900.1250\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 813444.0240 - reconstruction_loss: 13543.8721 - vqvae_loss: 799900.1250\n",
            "Epoch 1840/2500\n",
            "Epoch 1840/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 811057.2091 - reconstruction_loss: 13469.8740 - vqvae_loss: 797415.3329\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 811057.2091 - reconstruction_loss: 13469.8740 - vqvae_loss: 797415.3329\n",
            "Epoch 1841/2500\n",
            "Epoch 1841/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 809615.3269 - reconstruction_loss: 13457.0469 - vqvae_loss: 796181.5192\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 809615.3269 - reconstruction_loss: 13457.0469 - vqvae_loss: 796181.5192\n",
            "Epoch 1842/2500\n",
            "Epoch 1842/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 822277.0300 - reconstruction_loss: 13448.9727 - vqvae_loss: 808786.4688\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 822277.0300 - reconstruction_loss: 13448.9727 - vqvae_loss: 808786.4688\n",
            "Epoch 1843/2500\n",
            "Epoch 1843/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 815963.4760 - reconstruction_loss: 13498.3408 - vqvae_loss: 802436.5962\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 815963.4760 - reconstruction_loss: 13498.3408 - vqvae_loss: 802436.5962\n",
            "Epoch 1844/2500\n",
            "Epoch 1844/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 826524.6370 - reconstruction_loss: 13493.0430 - vqvae_loss: 813088.1995\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 826524.6370 - reconstruction_loss: 13493.0430 - vqvae_loss: 813088.1995\n",
            "Epoch 1845/2500\n",
            "Epoch 1845/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 831907.7740 - reconstruction_loss: 13595.1680 - vqvae_loss: 818375.9579\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 831907.7740 - reconstruction_loss: 13595.1680 - vqvae_loss: 818375.9579\n",
            "Epoch 1846/2500\n",
            "Epoch 1846/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 816019.6587 - reconstruction_loss: 13479.7393 - vqvae_loss: 802552.8990\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 816019.6587 - reconstruction_loss: 13479.7393 - vqvae_loss: 802552.8990\n",
            "Epoch 1847/2500\n",
            "Epoch 1847/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 821981.6094 - reconstruction_loss: 13483.8164 - vqvae_loss: 808607.3053\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 821981.6094 - reconstruction_loss: 13483.8164 - vqvae_loss: 808607.3053\n",
            "Epoch 1848/2500\n",
            "Epoch 1848/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 846870.1647 - reconstruction_loss: 13570.5342 - vqvae_loss: 833189.8858\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 846870.1647 - reconstruction_loss: 13570.5342 - vqvae_loss: 833189.8858\n",
            "Epoch 1849/2500\n",
            "Epoch 1849/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 846965.0108 - reconstruction_loss: 13499.4922 - vqvae_loss: 833347.0938\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 846965.0108 - reconstruction_loss: 13499.4922 - vqvae_loss: 833347.0938\n",
            "Epoch 1850/2500\n",
            "Epoch 1850/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 829584.8365 - reconstruction_loss: 13525.7988 - vqvae_loss: 816066.8750\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 829584.8365 - reconstruction_loss: 13525.7988 - vqvae_loss: 816066.8750\n",
            "Epoch 1851/2500\n",
            "Epoch 1851/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 833501.8173 - reconstruction_loss: 13484.6221 - vqvae_loss: 820061.2452\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 833501.8173 - reconstruction_loss: 13484.6221 - vqvae_loss: 820061.2452\n",
            "Epoch 1852/2500\n",
            "Epoch 1852/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 829870.4808 - reconstruction_loss: 13486.3516 - vqvae_loss: 816332.9135\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 829870.4808 - reconstruction_loss: 13486.3516 - vqvae_loss: 816332.9135\n",
            "Epoch 1853/2500\n",
            "Epoch 1853/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 826551.5000 - reconstruction_loss: 13490.9287 - vqvae_loss: 813132.1611\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 826551.5000 - reconstruction_loss: 13490.9287 - vqvae_loss: 813132.1611\n",
            "Epoch 1854/2500\n",
            "Epoch 1854/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 826246.6707 - reconstruction_loss: 13477.8047 - vqvae_loss: 812812.4688\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 826246.6707 - reconstruction_loss: 13477.8047 - vqvae_loss: 812812.4688\n",
            "Epoch 1855/2500\n",
            "Epoch 1855/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 836284.0685 - reconstruction_loss: 13472.1494 - vqvae_loss: 822946.8954\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 836284.0685 - reconstruction_loss: 13472.1494 - vqvae_loss: 822946.8954\n",
            "Epoch 1856/2500\n",
            "Epoch 1856/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 830340.2632 - reconstruction_loss: 13482.3652 - vqvae_loss: 816755.0433\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 830340.2632 - reconstruction_loss: 13482.3652 - vqvae_loss: 816755.0433\n",
            "Epoch 1857/2500\n",
            "Epoch 1857/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 825471.9603 - reconstruction_loss: 13455.2578 - vqvae_loss: 811976.5469\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 825471.9603 - reconstruction_loss: 13455.2578 - vqvae_loss: 811976.5469\n",
            "Epoch 1858/2500\n",
            "Epoch 1858/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 827638.2656 - reconstruction_loss: 13476.0029 - vqvae_loss: 814102.1935\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 827638.2656 - reconstruction_loss: 13476.0029 - vqvae_loss: 814102.1935\n",
            "Epoch 1859/2500\n",
            "Epoch 1859/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 825780.1827 - reconstruction_loss: 13465.7578 - vqvae_loss: 812352.3401\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 825780.1827 - reconstruction_loss: 13465.7578 - vqvae_loss: 812352.3401\n",
            "Epoch 1860/2500\n",
            "Epoch 1860/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 824045.0733 - reconstruction_loss: 13508.6484 - vqvae_loss: 810494.5901\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 824045.0733 - reconstruction_loss: 13508.6484 - vqvae_loss: 810494.5901\n",
            "Epoch 1861/2500\n",
            "Epoch 1861/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824096.0481 - reconstruction_loss: 13489.4648 - vqvae_loss: 810728.1659\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824096.0481 - reconstruction_loss: 13489.4648 - vqvae_loss: 810728.1659\n",
            "Epoch 1862/2500\n",
            "Epoch 1862/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 820582.0012 - reconstruction_loss: 13462.6338 - vqvae_loss: 807123.6851\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 820582.0012 - reconstruction_loss: 13462.6338 - vqvae_loss: 807123.6851\n",
            "Epoch 1863/2500\n",
            "Epoch 1863/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 822853.2825 - reconstruction_loss: 13445.2012 - vqvae_loss: 809525.7091\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 822853.2825 - reconstruction_loss: 13445.2012 - vqvae_loss: 809525.7091\n",
            "Epoch 1864/2500\n",
            "Epoch 1864/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 822908.9904 - reconstruction_loss: 13466.7227 - vqvae_loss: 809477.3966\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 822908.9904 - reconstruction_loss: 13466.7227 - vqvae_loss: 809477.3966\n",
            "Epoch 1865/2500\n",
            "Epoch 1865/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 822732.2212 - reconstruction_loss: 13400.5381 - vqvae_loss: 809212.8558\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 822732.2212 - reconstruction_loss: 13400.5381 - vqvae_loss: 809212.8558\n",
            "Epoch 1866/2500\n",
            "Epoch 1866/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 817471.6550 - reconstruction_loss: 13485.0137 - vqvae_loss: 803995.3558\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 817471.6550 - reconstruction_loss: 13485.0137 - vqvae_loss: 803995.3558\n",
            "Epoch 1867/2500\n",
            "Epoch 1867/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823227.6418 - reconstruction_loss: 13452.4189 - vqvae_loss: 809748.6719\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823227.6418 - reconstruction_loss: 13452.4189 - vqvae_loss: 809748.6719\n",
            "Epoch 1868/2500\n",
            "Epoch 1868/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 815142.8702 - reconstruction_loss: 13471.9805 - vqvae_loss: 801697.6671\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 815142.8702 - reconstruction_loss: 13471.9805 - vqvae_loss: 801697.6671\n",
            "Epoch 1869/2500\n",
            "Epoch 1869/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 824210.4663 - reconstruction_loss: 13420.0615 - vqvae_loss: 810829.9892\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 824210.4663 - reconstruction_loss: 13420.0615 - vqvae_loss: 810829.9892\n",
            "Epoch 1870/2500\n",
            "Epoch 1870/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 829871.4904 - reconstruction_loss: 13474.9619 - vqvae_loss: 816431.0385\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 829871.4904 - reconstruction_loss: 13474.9619 - vqvae_loss: 816431.0385\n",
            "Epoch 1871/2500\n",
            "Epoch 1871/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 821500.7680 - reconstruction_loss: 13456.2344 - vqvae_loss: 808097.2560\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 821500.7680 - reconstruction_loss: 13456.2344 - vqvae_loss: 808097.2560\n",
            "Epoch 1872/2500\n",
            "Epoch 1872/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 829232.6262 - reconstruction_loss: 13449.8877 - vqvae_loss: 815729.1214\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 829232.6262 - reconstruction_loss: 13449.8877 - vqvae_loss: 815729.1214\n",
            "Epoch 1873/2500\n",
            "Epoch 1873/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 827304.2981 - reconstruction_loss: 13475.1025 - vqvae_loss: 813855.9663\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 827304.2981 - reconstruction_loss: 13475.1025 - vqvae_loss: 813855.9663\n",
            "Epoch 1874/2500\n",
            "Epoch 1874/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 833721.6274 - reconstruction_loss: 13460.6914 - vqvae_loss: 820243.5505\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 833721.6274 - reconstruction_loss: 13460.6914 - vqvae_loss: 820243.5505\n",
            "Epoch 1875/2500\n",
            "Epoch 1875/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 829673.3642 - reconstruction_loss: 13430.9102 - vqvae_loss: 816211.9748\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 829673.3642 - reconstruction_loss: 13430.9102 - vqvae_loss: 816211.9748\n",
            "Epoch 1876/2500\n",
            "Epoch 1876/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 823703.7800 - reconstruction_loss: 13457.2793 - vqvae_loss: 810301.0913\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 823703.7800 - reconstruction_loss: 13457.2793 - vqvae_loss: 810301.0913\n",
            "Epoch 1877/2500\n",
            "Epoch 1877/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 844465.6562 - reconstruction_loss: 13473.7012 - vqvae_loss: 830943.8618\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 844465.6562 - reconstruction_loss: 13473.7012 - vqvae_loss: 830943.8618\n",
            "Epoch 1878/2500\n",
            "Epoch 1878/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 827642.5769 - reconstruction_loss: 13420.0596 - vqvae_loss: 814276.6755\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 827642.5769 - reconstruction_loss: 13420.0596 - vqvae_loss: 814276.6755\n",
            "Epoch 1879/2500\n",
            "Epoch 1879/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 831474.4423 - reconstruction_loss: 13420.6621 - vqvae_loss: 817990.5541\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 831474.4423 - reconstruction_loss: 13420.6621 - vqvae_loss: 817990.5541\n",
            "Epoch 1880/2500\n",
            "Epoch 1880/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 829796.0120 - reconstruction_loss: 13433.3828 - vqvae_loss: 816250.5889\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 829796.0120 - reconstruction_loss: 13433.3828 - vqvae_loss: 816250.5889\n",
            "Epoch 1881/2500\n",
            "Epoch 1881/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 825509.9255 - reconstruction_loss: 13476.5850 - vqvae_loss: 811965.2007\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 825509.9255 - reconstruction_loss: 13476.5850 - vqvae_loss: 811965.2007\n",
            "Epoch 1882/2500\n",
            "Epoch 1882/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 823287.3413 - reconstruction_loss: 13440.1797 - vqvae_loss: 809841.2981\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 823287.3413 - reconstruction_loss: 13440.1797 - vqvae_loss: 809841.2981\n",
            "Epoch 1883/2500\n",
            "Epoch 1883/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826183.8185 - reconstruction_loss: 13427.1621 - vqvae_loss: 812765.0469\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 826183.8185 - reconstruction_loss: 13427.1621 - vqvae_loss: 812765.0469\n",
            "Epoch 1884/2500\n",
            "Epoch 1884/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 820118.5192 - reconstruction_loss: 13400.0352 - vqvae_loss: 806688.0288\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 820118.5192 - reconstruction_loss: 13400.0352 - vqvae_loss: 806688.0288\n",
            "Epoch 1885/2500\n",
            "Epoch 1885/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823362.2019 - reconstruction_loss: 13403.5576 - vqvae_loss: 809955.3978\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 823362.2019 - reconstruction_loss: 13403.5576 - vqvae_loss: 809955.3978\n",
            "Epoch 1886/2500\n",
            "Epoch 1886/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 814842.3377 - reconstruction_loss: 13414.1377 - vqvae_loss: 801456.3750\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 814842.3377 - reconstruction_loss: 13414.1377 - vqvae_loss: 801456.3750\n",
            "Epoch 1887/2500\n",
            "Epoch 1887/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 819769.7224 - reconstruction_loss: 13423.9277 - vqvae_loss: 806282.1286\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 819769.7224 - reconstruction_loss: 13423.9277 - vqvae_loss: 806282.1286\n",
            "Epoch 1888/2500\n",
            "Epoch 1888/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 827097.6851 - reconstruction_loss: 13427.6533 - vqvae_loss: 813561.7007\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 827097.6851 - reconstruction_loss: 13427.6533 - vqvae_loss: 813561.7007\n",
            "Epoch 1889/2500\n",
            "Epoch 1889/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 819860.2260 - reconstruction_loss: 13361.4600 - vqvae_loss: 806498.8293\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 819860.2260 - reconstruction_loss: 13361.4600 - vqvae_loss: 806498.8293\n",
            "Epoch 1890/2500\n",
            "Epoch 1890/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 830735.5529 - reconstruction_loss: 13382.6104 - vqvae_loss: 817389.0349\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 830735.5529 - reconstruction_loss: 13382.6104 - vqvae_loss: 817389.0349\n",
            "Epoch 1891/2500\n",
            "Epoch 1891/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 832061.6118 - reconstruction_loss: 13457.8711 - vqvae_loss: 818614.2127\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 832061.6118 - reconstruction_loss: 13457.8711 - vqvae_loss: 818614.2127\n",
            "Epoch 1892/2500\n",
            "Epoch 1892/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 817938.3017 - reconstruction_loss: 13435.5479 - vqvae_loss: 804455.4856\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 817938.3017 - reconstruction_loss: 13435.5479 - vqvae_loss: 804455.4856\n",
            "Epoch 1893/2500\n",
            "Epoch 1893/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 808330.2031 - reconstruction_loss: 13407.2705 - vqvae_loss: 794916.4904\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 808330.2031 - reconstruction_loss: 13407.2705 - vqvae_loss: 794916.4904\n",
            "Epoch 1894/2500\n",
            "Epoch 1894/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 806996.3990 - reconstruction_loss: 13375.9844 - vqvae_loss: 793592.0601\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 806996.3990 - reconstruction_loss: 13375.9844 - vqvae_loss: 793592.0601\n",
            "Epoch 1895/2500\n",
            "Epoch 1895/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 792845.0889 - reconstruction_loss: 13366.6152 - vqvae_loss: 779567.3125\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 792845.0889 - reconstruction_loss: 13366.6152 - vqvae_loss: 779567.3125\n",
            "Epoch 1896/2500\n",
            "Epoch 1896/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 802062.5012 - reconstruction_loss: 13392.5186 - vqvae_loss: 788739.4423\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 802062.5012 - reconstruction_loss: 13392.5186 - vqvae_loss: 788739.4423\n",
            "Epoch 1897/2500\n",
            "Epoch 1897/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798990.4038 - reconstruction_loss: 13384.3154 - vqvae_loss: 785586.3077\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798990.4038 - reconstruction_loss: 13384.3154 - vqvae_loss: 785586.3077\n",
            "Epoch 1898/2500\n",
            "Epoch 1898/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798558.4519 - reconstruction_loss: 13402.1406 - vqvae_loss: 785191.4892\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798558.4519 - reconstruction_loss: 13402.1406 - vqvae_loss: 785191.4892\n",
            "Epoch 1899/2500\n",
            "Epoch 1899/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 794084.9207 - reconstruction_loss: 13361.3672 - vqvae_loss: 780799.4087\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 794084.9207 - reconstruction_loss: 13361.3672 - vqvae_loss: 780799.4087\n",
            "Epoch 1900/2500\n",
            "Epoch 1900/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798556.2416 - reconstruction_loss: 13402.9941 - vqvae_loss: 785269.7344\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798556.2416 - reconstruction_loss: 13402.9941 - vqvae_loss: 785269.7344\n",
            "Epoch 1901/2500\n",
            "Epoch 1901/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 797722.8522 - reconstruction_loss: 13401.8477 - vqvae_loss: 784335.7572\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 797722.8522 - reconstruction_loss: 13401.8477 - vqvae_loss: 784335.7572\n",
            "Epoch 1902/2500\n",
            "Epoch 1902/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 797908.8630 - reconstruction_loss: 13410.4482 - vqvae_loss: 784415.0998\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 797908.8630 - reconstruction_loss: 13410.4482 - vqvae_loss: 784415.0998\n",
            "Epoch 1903/2500\n",
            "Epoch 1903/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 804637.9471 - reconstruction_loss: 13396.1885 - vqvae_loss: 791235.4712\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 804637.9471 - reconstruction_loss: 13396.1885 - vqvae_loss: 791235.4712\n",
            "Epoch 1904/2500\n",
            "Epoch 1904/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 797309.5889 - reconstruction_loss: 13398.6436 - vqvae_loss: 783951.2728\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 797309.5889 - reconstruction_loss: 13398.6436 - vqvae_loss: 783951.2728\n",
            "Epoch 1905/2500\n",
            "Epoch 1905/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788823.4952 - reconstruction_loss: 13374.5469 - vqvae_loss: 775410.9868\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788823.4952 - reconstruction_loss: 13374.5469 - vqvae_loss: 775410.9868\n",
            "Epoch 1906/2500\n",
            "Epoch 1906/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 787728.7272 - reconstruction_loss: 13364.6201 - vqvae_loss: 774327.9111\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 787728.7272 - reconstruction_loss: 13364.6201 - vqvae_loss: 774327.9111\n",
            "Epoch 1907/2500\n",
            "Epoch 1907/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 782929.5938 - reconstruction_loss: 13413.8135 - vqvae_loss: 769456.4615\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 782929.5938 - reconstruction_loss: 13413.8135 - vqvae_loss: 769456.4615\n",
            "Epoch 1908/2500\n",
            "Epoch 1908/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 781091.4495 - reconstruction_loss: 13373.7441 - vqvae_loss: 767671.2392\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 781091.4495 - reconstruction_loss: 13373.7441 - vqvae_loss: 767671.2392\n",
            "Epoch 1909/2500\n",
            "Epoch 1909/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 781814.1226 - reconstruction_loss: 13377.0400 - vqvae_loss: 768298.8594\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 781814.1226 - reconstruction_loss: 13377.0400 - vqvae_loss: 768298.8594\n",
            "Epoch 1910/2500\n",
            "Epoch 1910/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 771474.4627 - reconstruction_loss: 13342.4619 - vqvae_loss: 758163.7139\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 771474.4627 - reconstruction_loss: 13342.4619 - vqvae_loss: 758163.7139\n",
            "Epoch 1911/2500\n",
            "Epoch 1911/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 779839.8870 - reconstruction_loss: 13381.4824 - vqvae_loss: 766477.3029\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 779839.8870 - reconstruction_loss: 13381.4824 - vqvae_loss: 766477.3029\n",
            "Epoch 1912/2500\n",
            "Epoch 1912/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 778212.3798 - reconstruction_loss: 13407.9619 - vqvae_loss: 764815.3510\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 778212.3798 - reconstruction_loss: 13407.9619 - vqvae_loss: 764815.3510\n",
            "Epoch 1913/2500\n",
            "Epoch 1913/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 770904.5901 - reconstruction_loss: 13399.7852 - vqvae_loss: 757535.7764\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 770904.5901 - reconstruction_loss: 13399.7852 - vqvae_loss: 757535.7764\n",
            "Epoch 1914/2500\n",
            "Epoch 1914/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 774361.0865 - reconstruction_loss: 13390.8809 - vqvae_loss: 760999.9351\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 774361.0865 - reconstruction_loss: 13390.8809 - vqvae_loss: 760999.9351\n",
            "Epoch 1915/2500\n",
            "Epoch 1915/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 773038.1959 - reconstruction_loss: 13347.9658 - vqvae_loss: 759756.8846\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 773038.1959 - reconstruction_loss: 13347.9658 - vqvae_loss: 759756.8846\n",
            "Epoch 1916/2500\n",
            "Epoch 1916/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771910.9880 - reconstruction_loss: 13336.3779 - vqvae_loss: 758576.0962\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771910.9880 - reconstruction_loss: 13336.3779 - vqvae_loss: 758576.0962\n",
            "Epoch 1917/2500\n",
            "Epoch 1917/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773686.7728 - reconstruction_loss: 13335.7158 - vqvae_loss: 760298.1130\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773686.7728 - reconstruction_loss: 13335.7158 - vqvae_loss: 760298.1130\n",
            "Epoch 1918/2500\n",
            "Epoch 1918/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 773238.5853 - reconstruction_loss: 13288.9775 - vqvae_loss: 759953.2476\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 773238.5853 - reconstruction_loss: 13288.9775 - vqvae_loss: 759953.2476\n",
            "Epoch 1919/2500\n",
            "Epoch 1919/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 759767.9988 - reconstruction_loss: 13317.8721 - vqvae_loss: 746383.1094\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 759767.9988 - reconstruction_loss: 13317.8721 - vqvae_loss: 746383.1094\n",
            "Epoch 1920/2500\n",
            "Epoch 1920/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 767108.7079 - reconstruction_loss: 13283.8125 - vqvae_loss: 753926.8522\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 767108.7079 - reconstruction_loss: 13283.8125 - vqvae_loss: 753926.8522\n",
            "Epoch 1921/2500\n",
            "Epoch 1921/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772092.6166 - reconstruction_loss: 13354.4746 - vqvae_loss: 758765.4988\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772092.6166 - reconstruction_loss: 13354.4746 - vqvae_loss: 758765.4988\n",
            "Epoch 1922/2500\n",
            "Epoch 1922/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 763266.1659 - reconstruction_loss: 13334.1201 - vqvae_loss: 749984.3954\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 763266.1659 - reconstruction_loss: 13334.1201 - vqvae_loss: 749984.3954\n",
            "Epoch 1923/2500\n",
            "Epoch 1923/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 756036.5361 - reconstruction_loss: 13320.2148 - vqvae_loss: 742739.7212\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 756036.5361 - reconstruction_loss: 13320.2148 - vqvae_loss: 742739.7212\n",
            "Epoch 1924/2500\n",
            "Epoch 1924/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 757189.5637 - reconstruction_loss: 13267.4268 - vqvae_loss: 743898.3029\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 757189.5637 - reconstruction_loss: 13267.4268 - vqvae_loss: 743898.3029\n",
            "Epoch 1925/2500\n",
            "Epoch 1925/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 747478.7055 - reconstruction_loss: 13299.6006 - vqvae_loss: 734207.8341\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 747478.7055 - reconstruction_loss: 13299.6006 - vqvae_loss: 734207.8341\n",
            "Epoch 1926/2500\n",
            "Epoch 1926/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 743998.3630 - reconstruction_loss: 13269.1689 - vqvae_loss: 730755.3834\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 743998.3630 - reconstruction_loss: 13269.1689 - vqvae_loss: 730755.3834\n",
            "Epoch 1927/2500\n",
            "Epoch 1927/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 752001.7428 - reconstruction_loss: 13322.2939 - vqvae_loss: 738511.9904\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 752001.7428 - reconstruction_loss: 13322.2939 - vqvae_loss: 738511.9904\n",
            "Epoch 1928/2500\n",
            "Epoch 1928/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 742928.5938 - reconstruction_loss: 13328.0674 - vqvae_loss: 729621.8450\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 742928.5938 - reconstruction_loss: 13328.0674 - vqvae_loss: 729621.8450\n",
            "Epoch 1929/2500\n",
            "Epoch 1929/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 756641.6587 - reconstruction_loss: 13342.3672 - vqvae_loss: 743282.0661\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 756641.6587 - reconstruction_loss: 13342.3672 - vqvae_loss: 743282.0661\n",
            "Epoch 1930/2500\n",
            "Epoch 1930/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 767516.5264 - reconstruction_loss: 13328.8564 - vqvae_loss: 754136.1430\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 767516.5264 - reconstruction_loss: 13328.8564 - vqvae_loss: 754136.1430\n",
            "Epoch 1931/2500\n",
            "Epoch 1931/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760031.9447 - reconstruction_loss: 13350.9121 - vqvae_loss: 746680.1238\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760031.9447 - reconstruction_loss: 13350.9121 - vqvae_loss: 746680.1238\n",
            "Epoch 1932/2500\n",
            "Epoch 1932/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 751069.6202 - reconstruction_loss: 13246.0967 - vqvae_loss: 737836.9483\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 751069.6202 - reconstruction_loss: 13246.0967 - vqvae_loss: 737836.9483\n",
            "Epoch 1933/2500\n",
            "Epoch 1933/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 740448.6166 - reconstruction_loss: 13278.8750 - vqvae_loss: 727194.0673\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 740448.6166 - reconstruction_loss: 13278.8750 - vqvae_loss: 727194.0673\n",
            "Epoch 1934/2500\n",
            "Epoch 1934/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 741911.6887 - reconstruction_loss: 13244.0000 - vqvae_loss: 728689.9339\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 741911.6887 - reconstruction_loss: 13244.0000 - vqvae_loss: 728689.9339\n",
            "Epoch 1935/2500\n",
            "Epoch 1935/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 741610.9615 - reconstruction_loss: 13242.2344 - vqvae_loss: 728394.0950\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 741610.9615 - reconstruction_loss: 13242.2344 - vqvae_loss: 728394.0950\n",
            "Epoch 1936/2500\n",
            "Epoch 1936/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 745689.7885 - reconstruction_loss: 13228.6406 - vqvae_loss: 732462.5601\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 745689.7885 - reconstruction_loss: 13228.6406 - vqvae_loss: 732462.5601\n",
            "Epoch 1937/2500\n",
            "Epoch 1937/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 740350.2764 - reconstruction_loss: 13160.8848 - vqvae_loss: 727227.9459\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 740350.2764 - reconstruction_loss: 13160.8848 - vqvae_loss: 727227.9459\n",
            "Epoch 1938/2500\n",
            "Epoch 1938/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 738183.1647 - reconstruction_loss: 13202.3594 - vqvae_loss: 724977.9615\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 738183.1647 - reconstruction_loss: 13202.3594 - vqvae_loss: 724977.9615\n",
            "Epoch 1939/2500\n",
            "Epoch 1939/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 737079.1562 - reconstruction_loss: 13212.6328 - vqvae_loss: 723751.8353\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 737079.1562 - reconstruction_loss: 13212.6328 - vqvae_loss: 723751.8353\n",
            "Epoch 1940/2500\n",
            "Epoch 1940/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 727717.5108 - reconstruction_loss: 13203.2197 - vqvae_loss: 714487.8413\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 727717.5108 - reconstruction_loss: 13203.2197 - vqvae_loss: 714487.8413\n",
            "Epoch 1941/2500\n",
            "Epoch 1941/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 729278.9639 - reconstruction_loss: 13193.2930 - vqvae_loss: 716086.8161\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 729278.9639 - reconstruction_loss: 13193.2930 - vqvae_loss: 716086.8161\n",
            "Epoch 1942/2500\n",
            "Epoch 1942/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 718570.0841 - reconstruction_loss: 13180.8252 - vqvae_loss: 705339.6298\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 718570.0841 - reconstruction_loss: 13180.8252 - vqvae_loss: 705339.6298\n",
            "Epoch 1943/2500\n",
            "Epoch 1943/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 728853.0180 - reconstruction_loss: 13218.8232 - vqvae_loss: 715607.5228\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 728853.0180 - reconstruction_loss: 13218.8232 - vqvae_loss: 715607.5228\n",
            "Epoch 1944/2500\n",
            "Epoch 1944/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 724222.5541 - reconstruction_loss: 13190.3701 - vqvae_loss: 710994.4519\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 724222.5541 - reconstruction_loss: 13190.3701 - vqvae_loss: 710994.4519\n",
            "Epoch 1945/2500\n",
            "Epoch 1945/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 723606.0096 - reconstruction_loss: 13184.8750 - vqvae_loss: 710357.1238\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 723606.0096 - reconstruction_loss: 13184.8750 - vqvae_loss: 710357.1238\n",
            "Epoch 1946/2500\n",
            "Epoch 1946/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 712192.5132 - reconstruction_loss: 13191.4873 - vqvae_loss: 699050.9639\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 712192.5132 - reconstruction_loss: 13191.4873 - vqvae_loss: 699050.9639\n",
            "Epoch 1947/2500\n",
            "Epoch 1947/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 716247.9387 - reconstruction_loss: 13201.3789 - vqvae_loss: 703122.7248\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 716247.9387 - reconstruction_loss: 13201.3789 - vqvae_loss: 703122.7248\n",
            "Epoch 1948/2500\n",
            "Epoch 1948/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 710513.0373 - reconstruction_loss: 13193.0342 - vqvae_loss: 697266.8906\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 710513.0373 - reconstruction_loss: 13193.0342 - vqvae_loss: 697266.8906\n",
            "Epoch 1949/2500\n",
            "Epoch 1949/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 705184.1875 - reconstruction_loss: 13225.4072 - vqvae_loss: 691972.1262\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 705184.1875 - reconstruction_loss: 13225.4072 - vqvae_loss: 691972.1262\n",
            "Epoch 1950/2500\n",
            "Epoch 1950/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 712280.7416 - reconstruction_loss: 13204.9346 - vqvae_loss: 699104.1514\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 712280.7416 - reconstruction_loss: 13204.9346 - vqvae_loss: 699104.1514\n",
            "Epoch 1951/2500\n",
            "Epoch 1951/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 721110.6106 - reconstruction_loss: 13208.6260 - vqvae_loss: 707826.7224\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 721110.6106 - reconstruction_loss: 13208.6260 - vqvae_loss: 707826.7224\n",
            "Epoch 1952/2500\n",
            "Epoch 1952/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 715386.0361 - reconstruction_loss: 13227.0576 - vqvae_loss: 702039.9579\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 715386.0361 - reconstruction_loss: 13227.0576 - vqvae_loss: 702039.9579\n",
            "Epoch 1953/2500\n",
            "Epoch 1953/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 710005.6815 - reconstruction_loss: 13208.1104 - vqvae_loss: 696834.4724\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 710005.6815 - reconstruction_loss: 13208.1104 - vqvae_loss: 696834.4724\n",
            "Epoch 1954/2500\n",
            "Epoch 1954/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 700885.6887 - reconstruction_loss: 13170.7842 - vqvae_loss: 687746.9760\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 700885.6887 - reconstruction_loss: 13170.7842 - vqvae_loss: 687746.9760\n",
            "Epoch 1955/2500\n",
            "Epoch 1955/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 697184.4880 - reconstruction_loss: 13161.9648 - vqvae_loss: 683992.6695\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 697184.4880 - reconstruction_loss: 13161.9648 - vqvae_loss: 683992.6695\n",
            "Epoch 1956/2500\n",
            "Epoch 1956/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 697734.5661 - reconstruction_loss: 13238.8711 - vqvae_loss: 684606.7452\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 697734.5661 - reconstruction_loss: 13238.8711 - vqvae_loss: 684606.7452\n",
            "Epoch 1957/2500\n",
            "Epoch 1957/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 703957.9760 - reconstruction_loss: 13255.0811 - vqvae_loss: 690664.0889\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 703957.9760 - reconstruction_loss: 13255.0811 - vqvae_loss: 690664.0889\n",
            "Epoch 1958/2500\n",
            "Epoch 1958/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 690341.0625 - reconstruction_loss: 13183.3828 - vqvae_loss: 677177.8329\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 690341.0625 - reconstruction_loss: 13183.3828 - vqvae_loss: 677177.8329\n",
            "Epoch 1959/2500\n",
            "Epoch 1959/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 683592.2091 - reconstruction_loss: 13227.9385 - vqvae_loss: 670366.1514\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 683592.2091 - reconstruction_loss: 13227.9385 - vqvae_loss: 670366.1514\n",
            "Epoch 1960/2500\n",
            "Epoch 1960/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 692043.9014 - reconstruction_loss: 13198.2100 - vqvae_loss: 678877.8498\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 692043.9014 - reconstruction_loss: 13198.2100 - vqvae_loss: 678877.8498\n",
            "Epoch 1961/2500\n",
            "Epoch 1961/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 681103.6310 - reconstruction_loss: 13204.1338 - vqvae_loss: 667835.3245\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 681103.6310 - reconstruction_loss: 13204.1338 - vqvae_loss: 667835.3245\n",
            "Epoch 1962/2500\n",
            "Epoch 1962/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 683299.4892 - reconstruction_loss: 13199.1484 - vqvae_loss: 670194.4663\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 683299.4892 - reconstruction_loss: 13199.1484 - vqvae_loss: 670194.4663\n",
            "Epoch 1963/2500\n",
            "Epoch 1963/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 674089.9603 - reconstruction_loss: 13194.5537 - vqvae_loss: 660980.2163\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 674089.9603 - reconstruction_loss: 13194.5537 - vqvae_loss: 660980.2163\n",
            "Epoch 1964/2500\n",
            "Epoch 1964/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 681037.1623 - reconstruction_loss: 13189.5176 - vqvae_loss: 667827.0745\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 681037.1623 - reconstruction_loss: 13189.5176 - vqvae_loss: 667827.0745\n",
            "Epoch 1965/2500\n",
            "Epoch 1965/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 677594.2728 - reconstruction_loss: 13232.2402 - vqvae_loss: 664421.2488\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 677594.2728 - reconstruction_loss: 13232.2402 - vqvae_loss: 664421.2488\n",
            "Epoch 1966/2500\n",
            "Epoch 1966/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 677920.9519 - reconstruction_loss: 13172.8252 - vqvae_loss: 664693.8197\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 677920.9519 - reconstruction_loss: 13172.8252 - vqvae_loss: 664693.8197\n",
            "Epoch 1967/2500\n",
            "Epoch 1967/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 671549.9639 - reconstruction_loss: 13194.3955 - vqvae_loss: 658404.5889\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 671549.9639 - reconstruction_loss: 13194.3955 - vqvae_loss: 658404.5889\n",
            "Epoch 1968/2500\n",
            "Epoch 1968/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 673825.5012 - reconstruction_loss: 13182.4521 - vqvae_loss: 660642.5312\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 673825.5012 - reconstruction_loss: 13182.4521 - vqvae_loss: 660642.5312\n",
            "Epoch 1969/2500\n",
            "Epoch 1969/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 674469.9892 - reconstruction_loss: 13231.0488 - vqvae_loss: 661431.0252\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 674469.9892 - reconstruction_loss: 13231.0488 - vqvae_loss: 661431.0252\n",
            "Epoch 1970/2500\n",
            "Epoch 1970/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 678347.9363 - reconstruction_loss: 13257.1348 - vqvae_loss: 665097.5637\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 678347.9363 - reconstruction_loss: 13257.1348 - vqvae_loss: 665097.5637\n",
            "Epoch 1971/2500\n",
            "Epoch 1971/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 674964.3125 - reconstruction_loss: 13377.0996 - vqvae_loss: 661643.2933\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 674964.3125 - reconstruction_loss: 13377.0996 - vqvae_loss: 661643.2933\n",
            "Epoch 1972/2500\n",
            "Epoch 1972/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 679285.4147 - reconstruction_loss: 13213.7451 - vqvae_loss: 666013.4387\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 679285.4147 - reconstruction_loss: 13213.7451 - vqvae_loss: 666013.4387\n",
            "Epoch 1973/2500\n",
            "Epoch 1973/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 679373.2175 - reconstruction_loss: 13225.3760 - vqvae_loss: 666189.7608\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 679373.2175 - reconstruction_loss: 13225.3760 - vqvae_loss: 666189.7608\n",
            "Epoch 1974/2500\n",
            "Epoch 1974/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 676221.0409 - reconstruction_loss: 13199.3711 - vqvae_loss: 663054.0889\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 676221.0409 - reconstruction_loss: 13199.3711 - vqvae_loss: 663054.0889\n",
            "Epoch 1975/2500\n",
            "Epoch 1975/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 679901.2416 - reconstruction_loss: 13236.3115 - vqvae_loss: 666570.5288\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 679901.2416 - reconstruction_loss: 13236.3115 - vqvae_loss: 666570.5288\n",
            "Epoch 1976/2500\n",
            "Epoch 1976/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 681369.6887 - reconstruction_loss: 13258.6484 - vqvae_loss: 668145.1550\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 681369.6887 - reconstruction_loss: 13258.6484 - vqvae_loss: 668145.1550\n",
            "Epoch 1977/2500\n",
            "Epoch 1977/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 672454.2921 - reconstruction_loss: 13257.9277 - vqvae_loss: 659195.4038\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 672454.2921 - reconstruction_loss: 13257.9277 - vqvae_loss: 659195.4038\n",
            "Epoch 1978/2500\n",
            "Epoch 1978/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 667428.5625 - reconstruction_loss: 13198.1045 - vqvae_loss: 654138.6070\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 667428.5625 - reconstruction_loss: 13198.1045 - vqvae_loss: 654138.6070\n",
            "Epoch 1979/2500\n",
            "Epoch 1979/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 666494.8990 - reconstruction_loss: 13182.7734 - vqvae_loss: 653332.9483\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 666494.8990 - reconstruction_loss: 13182.7734 - vqvae_loss: 653332.9483\n",
            "Epoch 1980/2500\n",
            "Epoch 1980/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 675332.7716 - reconstruction_loss: 13179.7559 - vqvae_loss: 662185.4916\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 675332.7716 - reconstruction_loss: 13179.7559 - vqvae_loss: 662185.4916\n",
            "Epoch 1981/2500\n",
            "Epoch 1981/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 668909.0817 - reconstruction_loss: 13166.4795 - vqvae_loss: 655862.9760\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 668909.0817 - reconstruction_loss: 13166.4795 - vqvae_loss: 655862.9760\n",
            "Epoch 1982/2500\n",
            "Epoch 1982/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 671452.0661 - reconstruction_loss: 13208.7002 - vqvae_loss: 658281.8762\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 671452.0661 - reconstruction_loss: 13208.7002 - vqvae_loss: 658281.8762\n",
            "Epoch 1983/2500\n",
            "Epoch 1983/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 662319.6743 - reconstruction_loss: 13238.5781 - vqvae_loss: 649090.5481\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 662319.6743 - reconstruction_loss: 13238.5781 - vqvae_loss: 649090.5481\n",
            "Epoch 1984/2500\n",
            "Epoch 1984/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 668706.7200 - reconstruction_loss: 13281.0762 - vqvae_loss: 655354.4038\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 668706.7200 - reconstruction_loss: 13281.0762 - vqvae_loss: 655354.4038\n",
            "Epoch 1985/2500\n",
            "Epoch 1985/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 662282.3534 - reconstruction_loss: 13274.0771 - vqvae_loss: 648960.3582\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 662282.3534 - reconstruction_loss: 13274.0771 - vqvae_loss: 648960.3582\n",
            "Epoch 1986/2500\n",
            "Epoch 1986/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 663035.1454 - reconstruction_loss: 13226.6045 - vqvae_loss: 649737.9832\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 663035.1454 - reconstruction_loss: 13226.6045 - vqvae_loss: 649737.9832\n",
            "Epoch 1987/2500\n",
            "Epoch 1987/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 658847.3594 - reconstruction_loss: 13202.7939 - vqvae_loss: 645624.7921\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 658847.3594 - reconstruction_loss: 13202.7939 - vqvae_loss: 645624.7921\n",
            "Epoch 1988/2500\n",
            "Epoch 1988/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 655099.9964 - reconstruction_loss: 13262.1641 - vqvae_loss: 641874.3486\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 655099.9964 - reconstruction_loss: 13262.1641 - vqvae_loss: 641874.3486\n",
            "Epoch 1989/2500\n",
            "Epoch 1989/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 662195.7308 - reconstruction_loss: 13262.0850 - vqvae_loss: 648925.2404\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 662195.7308 - reconstruction_loss: 13262.0850 - vqvae_loss: 648925.2404\n",
            "Epoch 1990/2500\n",
            "Epoch 1990/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 649313.4471 - reconstruction_loss: 13272.3555 - vqvae_loss: 636085.3413\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 649313.4471 - reconstruction_loss: 13272.3555 - vqvae_loss: 636085.3413\n",
            "Epoch 1991/2500\n",
            "Epoch 1991/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 657441.6310 - reconstruction_loss: 13270.4424 - vqvae_loss: 644182.9507\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 657441.6310 - reconstruction_loss: 13270.4424 - vqvae_loss: 644182.9507\n",
            "Epoch 1992/2500\n",
            "Epoch 1992/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 664091.5445 - reconstruction_loss: 13288.1045 - vqvae_loss: 650760.6274\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 664091.5445 - reconstruction_loss: 13288.1045 - vqvae_loss: 650760.6274\n",
            "Epoch 1993/2500\n",
            "Epoch 1993/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 659849.3882 - reconstruction_loss: 13236.9102 - vqvae_loss: 646494.6839\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 659849.3882 - reconstruction_loss: 13236.9102 - vqvae_loss: 646494.6839\n",
            "Epoch 1994/2500\n",
            "Epoch 1994/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 655426.7716 - reconstruction_loss: 13237.6504 - vqvae_loss: 642129.2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 655426.7716 - reconstruction_loss: 13237.6504 - vqvae_loss: 642129.2500\n",
            "Epoch 1995/2500\n",
            "Epoch 1995/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 647636.5385 - reconstruction_loss: 13274.8711 - vqvae_loss: 634374.4904\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 647636.5385 - reconstruction_loss: 13274.8711 - vqvae_loss: 634374.4904\n",
            "Epoch 1996/2500\n",
            "Epoch 1996/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 649265.2776 - reconstruction_loss: 13284.5850 - vqvae_loss: 635985.2620\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 649265.2776 - reconstruction_loss: 13284.5850 - vqvae_loss: 635985.2620\n",
            "Epoch 1997/2500\n",
            "Epoch 1997/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 653670.9243 - reconstruction_loss: 13291.9922 - vqvae_loss: 640365.6611\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 653670.9243 - reconstruction_loss: 13291.9922 - vqvae_loss: 640365.6611\n",
            "Epoch 1998/2500\n",
            "Epoch 1998/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 656723.4207 - reconstruction_loss: 13355.1436 - vqvae_loss: 643380.7692\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 656723.4207 - reconstruction_loss: 13355.1436 - vqvae_loss: 643380.7692\n",
            "Epoch 1999/2500\n",
            "Epoch 1999/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 657238.5072 - reconstruction_loss: 13273.3408 - vqvae_loss: 643859.5144\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 657238.5072 - reconstruction_loss: 13273.3408 - vqvae_loss: 643859.5144\n",
            "Epoch 2000/2500\n",
            "Epoch 2000/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 662616.2248 - reconstruction_loss: 13293.0723 - vqvae_loss: 649263.1118\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 662616.2248 - reconstruction_loss: 13293.0723 - vqvae_loss: 649263.1118\n",
            "Epoch 2001/2500\n",
            "Epoch 2001/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 667597.9663 - reconstruction_loss: 13240.3350 - vqvae_loss: 654280.4808\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 667597.9663 - reconstruction_loss: 13240.3350 - vqvae_loss: 654280.4808\n",
            "Epoch 2002/2500\n",
            "Epoch 2002/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 656350.8341 - reconstruction_loss: 13272.2656 - vqvae_loss: 643129.8077\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 656350.8341 - reconstruction_loss: 13272.2656 - vqvae_loss: 643129.8077\n",
            "Epoch 2003/2500\n",
            "Epoch 2003/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 650398.0673 - reconstruction_loss: 13272.1045 - vqvae_loss: 637118.0745\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 650398.0673 - reconstruction_loss: 13272.1045 - vqvae_loss: 637118.0745\n",
            "Epoch 2004/2500\n",
            "Epoch 2004/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 659800.4363 - reconstruction_loss: 13269.9668 - vqvae_loss: 646449.4243\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 659800.4363 - reconstruction_loss: 13269.9668 - vqvae_loss: 646449.4243\n",
            "Epoch 2005/2500\n",
            "Epoch 2005/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 650470.5168 - reconstruction_loss: 13274.7969 - vqvae_loss: 637198.8690\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 650470.5168 - reconstruction_loss: 13274.7969 - vqvae_loss: 637198.8690\n",
            "Epoch 2006/2500\n",
            "Epoch 2006/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 655580.2728 - reconstruction_loss: 13335.7217 - vqvae_loss: 642182.4207\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 655580.2728 - reconstruction_loss: 13335.7217 - vqvae_loss: 642182.4207\n",
            "Epoch 2007/2500\n",
            "Epoch 2007/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 658373.2332 - reconstruction_loss: 13313.4121 - vqvae_loss: 645004.7788\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 658373.2332 - reconstruction_loss: 13313.4121 - vqvae_loss: 645004.7788\n",
            "Epoch 2008/2500\n",
            "Epoch 2008/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 651272.4579 - reconstruction_loss: 13304.6201 - vqvae_loss: 638016.3329\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 651272.4579 - reconstruction_loss: 13304.6201 - vqvae_loss: 638016.3329\n",
            "Epoch 2009/2500\n",
            "Epoch 2009/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 665180.5409 - reconstruction_loss: 13289.2783 - vqvae_loss: 651960.5986\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 665180.5409 - reconstruction_loss: 13289.2783 - vqvae_loss: 651960.5986\n",
            "Epoch 2010/2500\n",
            "Epoch 2010/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 676078.5445 - reconstruction_loss: 13366.6113 - vqvae_loss: 662641.1298\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 676078.5445 - reconstruction_loss: 13366.6113 - vqvae_loss: 662641.1298\n",
            "Epoch 2011/2500\n",
            "Epoch 2011/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 658695.4171 - reconstruction_loss: 13289.1025 - vqvae_loss: 645454.0685\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 658695.4171 - reconstruction_loss: 13289.1025 - vqvae_loss: 645454.0685\n",
            "Epoch 2012/2500\n",
            "Epoch 2012/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 669583.6899 - reconstruction_loss: 13250.7842 - vqvae_loss: 656309.7812\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 669583.6899 - reconstruction_loss: 13250.7842 - vqvae_loss: 656309.7812\n",
            "Epoch 2013/2500\n",
            "Epoch 2013/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 671444.1298 - reconstruction_loss: 13290.7881 - vqvae_loss: 658228.8750\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 671444.1298 - reconstruction_loss: 13290.7881 - vqvae_loss: 658228.8750\n",
            "Epoch 2014/2500\n",
            "Epoch 2014/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 663466.3197 - reconstruction_loss: 13235.2217 - vqvae_loss: 650154.5397\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 663466.3197 - reconstruction_loss: 13235.2217 - vqvae_loss: 650154.5397\n",
            "Epoch 2015/2500\n",
            "Epoch 2015/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 654644.6935 - reconstruction_loss: 13300.4600 - vqvae_loss: 641399.5769\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 654644.6935 - reconstruction_loss: 13300.4600 - vqvae_loss: 641399.5769\n",
            "Epoch 2016/2500\n",
            "Epoch 2016/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 654477.2380 - reconstruction_loss: 13300.1787 - vqvae_loss: 641218.0349\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 654477.2380 - reconstruction_loss: 13300.1787 - vqvae_loss: 641218.0349\n",
            "Epoch 2017/2500\n",
            "Epoch 2017/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 667285.5974 - reconstruction_loss: 13342.1143 - vqvae_loss: 654111.5613\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 667285.5974 - reconstruction_loss: 13342.1143 - vqvae_loss: 654111.5613\n",
            "Epoch 2018/2500\n",
            "Epoch 2018/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 672732.3281 - reconstruction_loss: 13339.7275 - vqvae_loss: 659456.3185\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 672732.3281 - reconstruction_loss: 13339.7275 - vqvae_loss: 659456.3185\n",
            "Epoch 2019/2500\n",
            "Epoch 2019/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 674145.7596 - reconstruction_loss: 13261.4912 - vqvae_loss: 660808.6154\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 674145.7596 - reconstruction_loss: 13261.4912 - vqvae_loss: 660808.6154\n",
            "Epoch 2020/2500\n",
            "Epoch 2020/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 678148.6526 - reconstruction_loss: 13276.3252 - vqvae_loss: 664912.3702\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 678148.6526 - reconstruction_loss: 13276.3252 - vqvae_loss: 664912.3702\n",
            "Epoch 2021/2500\n",
            "Epoch 2021/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 677481.7488 - reconstruction_loss: 13248.7588 - vqvae_loss: 664265.3522\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 677481.7488 - reconstruction_loss: 13248.7588 - vqvae_loss: 664265.3522\n",
            "Epoch 2022/2500\n",
            "Epoch 2022/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 696019.1659 - reconstruction_loss: 13259.7002 - vqvae_loss: 682785.8365\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 696019.1659 - reconstruction_loss: 13259.7002 - vqvae_loss: 682785.8365\n",
            "Epoch 2023/2500\n",
            "Epoch 2023/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 694713.9014 - reconstruction_loss: 13233.8457 - vqvae_loss: 681390.9928\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 694713.9014 - reconstruction_loss: 13233.8457 - vqvae_loss: 681390.9928\n",
            "Epoch 2024/2500\n",
            "Epoch 2024/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 701031.3077 - reconstruction_loss: 13258.9746 - vqvae_loss: 687817.3498\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 701031.3077 - reconstruction_loss: 13258.9746 - vqvae_loss: 687817.3498\n",
            "Epoch 2025/2500\n",
            "Epoch 2025/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 706539.6695 - reconstruction_loss: 13248.9189 - vqvae_loss: 693254.8462\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 706539.6695 - reconstruction_loss: 13248.9189 - vqvae_loss: 693254.8462\n",
            "Epoch 2026/2500\n",
            "Epoch 2026/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 687621.6791 - reconstruction_loss: 13201.6719 - vqvae_loss: 674550.6058\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 687621.6791 - reconstruction_loss: 13201.6719 - vqvae_loss: 674550.6058\n",
            "Epoch 2027/2500\n",
            "Epoch 2027/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 700122.9772 - reconstruction_loss: 13196.1621 - vqvae_loss: 686919.0745\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 700122.9772 - reconstruction_loss: 13196.1621 - vqvae_loss: 686919.0745\n",
            "Epoch 2028/2500\n",
            "Epoch 2028/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 709661.3486 - reconstruction_loss: 13266.2178 - vqvae_loss: 696386.1034\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 709661.3486 - reconstruction_loss: 13266.2178 - vqvae_loss: 696386.1034\n",
            "Epoch 2029/2500\n",
            "Epoch 2029/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 694610.2284 - reconstruction_loss: 13265.0498 - vqvae_loss: 681378.9712\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 694610.2284 - reconstruction_loss: 13265.0498 - vqvae_loss: 681378.9712\n",
            "Epoch 2030/2500\n",
            "Epoch 2030/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 693717.6370 - reconstruction_loss: 13203.0400 - vqvae_loss: 680502.6514\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 693717.6370 - reconstruction_loss: 13203.0400 - vqvae_loss: 680502.6514\n",
            "Epoch 2031/2500\n",
            "Epoch 2031/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 700931.8858 - reconstruction_loss: 13186.4434 - vqvae_loss: 687758.7596\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 700931.8858 - reconstruction_loss: 13186.4434 - vqvae_loss: 687758.7596\n",
            "Epoch 2032/2500\n",
            "Epoch 2032/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 707837.5048 - reconstruction_loss: 13211.8252 - vqvae_loss: 694704.7404\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 707837.5048 - reconstruction_loss: 13211.8252 - vqvae_loss: 694704.7404\n",
            "Epoch 2033/2500\n",
            "Epoch 2033/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 726493.6070 - reconstruction_loss: 13303.2656 - vqvae_loss: 713124.2332\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 726493.6070 - reconstruction_loss: 13303.2656 - vqvae_loss: 713124.2332\n",
            "Epoch 2034/2500\n",
            "Epoch 2034/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 725051.0036 - reconstruction_loss: 13287.3047 - vqvae_loss: 711672.1839\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 725051.0036 - reconstruction_loss: 13287.3047 - vqvae_loss: 711672.1839\n",
            "Epoch 2035/2500\n",
            "Epoch 2035/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 713377.1082 - reconstruction_loss: 13275.2148 - vqvae_loss: 700029.4603\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 713377.1082 - reconstruction_loss: 13275.2148 - vqvae_loss: 700029.4603\n",
            "Epoch 2036/2500\n",
            "Epoch 2036/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 708051.6130 - reconstruction_loss: 13252.2324 - vqvae_loss: 694848.5024\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 708051.6130 - reconstruction_loss: 13252.2324 - vqvae_loss: 694848.5024\n",
            "Epoch 2037/2500\n",
            "Epoch 2037/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 716286.4844 - reconstruction_loss: 13254.1904 - vqvae_loss: 703045.2632\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 716286.4844 - reconstruction_loss: 13254.1904 - vqvae_loss: 703045.2632\n",
            "Epoch 2038/2500\n",
            "Epoch 2038/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 731051.8534 - reconstruction_loss: 13235.7637 - vqvae_loss: 717809.2007\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 731051.8534 - reconstruction_loss: 13235.7637 - vqvae_loss: 717809.2007\n",
            "Epoch 2039/2500\n",
            "Epoch 2039/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 726443.3101 - reconstruction_loss: 13251.7217 - vqvae_loss: 713187.0048\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 726443.3101 - reconstruction_loss: 13251.7217 - vqvae_loss: 713187.0048\n",
            "Epoch 2040/2500\n",
            "Epoch 2040/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 716522.7981 - reconstruction_loss: 13236.4746 - vqvae_loss: 703350.7680\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 716522.7981 - reconstruction_loss: 13236.4746 - vqvae_loss: 703350.7680\n",
            "Epoch 2041/2500\n",
            "Epoch 2041/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 725308.3558 - reconstruction_loss: 13306.1260 - vqvae_loss: 712065.5192\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 725308.3558 - reconstruction_loss: 13306.1260 - vqvae_loss: 712065.5192\n",
            "Epoch 2042/2500\n",
            "Epoch 2042/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 756887.4844 - reconstruction_loss: 13510.9756 - vqvae_loss: 743323.7151\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 756887.4844 - reconstruction_loss: 13510.9756 - vqvae_loss: 743323.7151\n",
            "Epoch 2043/2500\n",
            "Epoch 2043/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 746300.9555 - reconstruction_loss: 13436.0400 - vqvae_loss: 732771.3642\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 746300.9555 - reconstruction_loss: 13436.0400 - vqvae_loss: 732771.3642\n",
            "Epoch 2044/2500\n",
            "Epoch 2044/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 717250.3474 - reconstruction_loss: 13347.5625 - vqvae_loss: 703962.0913\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 717250.3474 - reconstruction_loss: 13347.5625 - vqvae_loss: 703962.0913\n",
            "Epoch 2045/2500\n",
            "Epoch 2045/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 721206.8377 - reconstruction_loss: 13424.9951 - vqvae_loss: 707757.6659\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 721206.8377 - reconstruction_loss: 13424.9951 - vqvae_loss: 707757.6659\n",
            "Epoch 2046/2500\n",
            "Epoch 2046/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 739842.9279 - reconstruction_loss: 13346.3799 - vqvae_loss: 726451.5925\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 739842.9279 - reconstruction_loss: 13346.3799 - vqvae_loss: 726451.5925\n",
            "Epoch 2047/2500\n",
            "Epoch 2047/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 735671.8594 - reconstruction_loss: 13380.8545 - vqvae_loss: 722253.4339\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 735671.8594 - reconstruction_loss: 13380.8545 - vqvae_loss: 722253.4339\n",
            "Epoch 2048/2500\n",
            "Epoch 2048/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 721019.5685 - reconstruction_loss: 13321.8721 - vqvae_loss: 707672.2981\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 721019.5685 - reconstruction_loss: 13321.8721 - vqvae_loss: 707672.2981\n",
            "Epoch 2049/2500\n",
            "Epoch 2049/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 742148.3846 - reconstruction_loss: 13375.5703 - vqvae_loss: 728737.1887\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 742148.3846 - reconstruction_loss: 13375.5703 - vqvae_loss: 728737.1887\n",
            "Epoch 2050/2500\n",
            "Epoch 2050/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 739593.4964 - reconstruction_loss: 13387.6094 - vqvae_loss: 726317.2404\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 739593.4964 - reconstruction_loss: 13387.6094 - vqvae_loss: 726317.2404\n",
            "Epoch 2051/2500\n",
            "Epoch 2051/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 743927.4988 - reconstruction_loss: 13368.9961 - vqvae_loss: 730490.6214\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 743927.4988 - reconstruction_loss: 13368.9961 - vqvae_loss: 730490.6214\n",
            "Epoch 2052/2500\n",
            "Epoch 2052/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 730404.0541 - reconstruction_loss: 13389.4512 - vqvae_loss: 717031.2380\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 730404.0541 - reconstruction_loss: 13389.4512 - vqvae_loss: 717031.2380\n",
            "Epoch 2053/2500\n",
            "Epoch 2053/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 754506.8882 - reconstruction_loss: 13422.2197 - vqvae_loss: 741038.0048\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 754506.8882 - reconstruction_loss: 13422.2197 - vqvae_loss: 741038.0048\n",
            "Epoch 2054/2500\n",
            "Epoch 2054/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739603.9147 - reconstruction_loss: 13401.8750 - vqvae_loss: 726237.1238\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739603.9147 - reconstruction_loss: 13401.8750 - vqvae_loss: 726237.1238\n",
            "Epoch 2055/2500\n",
            "Epoch 2055/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 747350.2800 - reconstruction_loss: 13439.3701 - vqvae_loss: 734006.9531\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 747350.2800 - reconstruction_loss: 13439.3701 - vqvae_loss: 734006.9531\n",
            "Epoch 2056/2500\n",
            "Epoch 2056/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 754943.9964 - reconstruction_loss: 13423.4717 - vqvae_loss: 741599.4123\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 754943.9964 - reconstruction_loss: 13423.4717 - vqvae_loss: 741599.4123\n",
            "Epoch 2057/2500\n",
            "Epoch 2057/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 771231.8594 - reconstruction_loss: 13392.7607 - vqvae_loss: 757894.2776\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 771231.8594 - reconstruction_loss: 13392.7607 - vqvae_loss: 757894.2776\n",
            "Epoch 2058/2500\n",
            "Epoch 2058/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768471.8197 - reconstruction_loss: 13442.6426 - vqvae_loss: 755023.7921\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768471.8197 - reconstruction_loss: 13442.6426 - vqvae_loss: 755023.7921\n",
            "Epoch 2059/2500\n",
            "Epoch 2059/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775803.0036 - reconstruction_loss: 13466.8135 - vqvae_loss: 762334.8978\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775803.0036 - reconstruction_loss: 13466.8135 - vqvae_loss: 762334.8978\n",
            "Epoch 2060/2500\n",
            "Epoch 2060/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 784765.9567 - reconstruction_loss: 13533.8613 - vqvae_loss: 771363.1082\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 784765.9567 - reconstruction_loss: 13533.8613 - vqvae_loss: 771363.1082\n",
            "Epoch 2061/2500\n",
            "Epoch 2061/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 787197.9495 - reconstruction_loss: 13465.6328 - vqvae_loss: 773672.1094\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 787197.9495 - reconstruction_loss: 13465.6328 - vqvae_loss: 773672.1094\n",
            "Epoch 2062/2500\n",
            "Epoch 2062/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 809208.1322 - reconstruction_loss: 13510.4961 - vqvae_loss: 795718.1635\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 809208.1322 - reconstruction_loss: 13510.4961 - vqvae_loss: 795718.1635\n",
            "Epoch 2063/2500\n",
            "Epoch 2063/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 804489.2103 - reconstruction_loss: 13494.2656 - vqvae_loss: 790961.3317\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 804489.2103 - reconstruction_loss: 13494.2656 - vqvae_loss: 790961.3317\n",
            "Epoch 2064/2500\n",
            "Epoch 2064/2500\n",
            "51/51 [==============================] - 5s 101ms/step - loss: 801885.2560 - reconstruction_loss: 13401.4531 - vqvae_loss: 788582.9279\n",
            "51/51 [==============================] - 5s 101ms/step - loss: 801885.2560 - reconstruction_loss: 13401.4531 - vqvae_loss: 788582.9279\n",
            "Epoch 2065/2500\n",
            "Epoch 2065/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 819830.2452 - reconstruction_loss: 13455.7705 - vqvae_loss: 806330.9567\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 819830.2452 - reconstruction_loss: 13455.7705 - vqvae_loss: 806330.9567\n",
            "Epoch 2066/2500\n",
            "Epoch 2066/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 828424.5781 - reconstruction_loss: 13435.4072 - vqvae_loss: 814910.9315\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 828424.5781 - reconstruction_loss: 13435.4072 - vqvae_loss: 814910.9315\n",
            "Epoch 2067/2500\n",
            "Epoch 2067/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 822258.5312 - reconstruction_loss: 13402.5811 - vqvae_loss: 808914.5853\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 822258.5312 - reconstruction_loss: 13402.5811 - vqvae_loss: 808914.5853\n",
            "Epoch 2068/2500\n",
            "Epoch 2068/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 828437.2512 - reconstruction_loss: 13426.7588 - vqvae_loss: 815004.7380\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 828437.2512 - reconstruction_loss: 13426.7588 - vqvae_loss: 815004.7380\n",
            "Epoch 2069/2500\n",
            "Epoch 2069/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 834195.7656 - reconstruction_loss: 13372.3906 - vqvae_loss: 820788.7236\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 834195.7656 - reconstruction_loss: 13372.3906 - vqvae_loss: 820788.7236\n",
            "Epoch 2070/2500\n",
            "Epoch 2070/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 839765.6935 - reconstruction_loss: 13434.3184 - vqvae_loss: 826427.5108\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 839765.6935 - reconstruction_loss: 13434.3184 - vqvae_loss: 826427.5108\n",
            "Epoch 2071/2500\n",
            "Epoch 2071/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 828181.7788 - reconstruction_loss: 13343.6641 - vqvae_loss: 814857.3978\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 828181.7788 - reconstruction_loss: 13343.6641 - vqvae_loss: 814857.3978\n",
            "Epoch 2072/2500\n",
            "Epoch 2072/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 849619.3978 - reconstruction_loss: 13344.3652 - vqvae_loss: 836254.3185\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 849619.3978 - reconstruction_loss: 13344.3652 - vqvae_loss: 836254.3185\n",
            "Epoch 2073/2500\n",
            "Epoch 2073/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 842803.5048 - reconstruction_loss: 13369.0498 - vqvae_loss: 829496.4495\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 842803.5048 - reconstruction_loss: 13369.0498 - vqvae_loss: 829496.4495\n",
            "Epoch 2074/2500\n",
            "Epoch 2074/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 849903.4904 - reconstruction_loss: 13340.6094 - vqvae_loss: 836534.8582\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 849903.4904 - reconstruction_loss: 13340.6094 - vqvae_loss: 836534.8582\n",
            "Epoch 2075/2500\n",
            "Epoch 2075/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 854979.3053 - reconstruction_loss: 13361.9150 - vqvae_loss: 841629.8534\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 854979.3053 - reconstruction_loss: 13361.9150 - vqvae_loss: 841629.8534\n",
            "Epoch 2076/2500\n",
            "Epoch 2076/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 844764.1202 - reconstruction_loss: 13406.2168 - vqvae_loss: 831355.6118\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 844764.1202 - reconstruction_loss: 13406.2168 - vqvae_loss: 831355.6118\n",
            "Epoch 2077/2500\n",
            "Epoch 2077/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 848352.1058 - reconstruction_loss: 13386.8301 - vqvae_loss: 834981.2163\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 848352.1058 - reconstruction_loss: 13386.8301 - vqvae_loss: 834981.2163\n",
            "Epoch 2078/2500\n",
            "Epoch 2078/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 849242.6142 - reconstruction_loss: 13382.7939 - vqvae_loss: 835794.6743\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 849242.6142 - reconstruction_loss: 13382.7939 - vqvae_loss: 835794.6743\n",
            "Epoch 2079/2500\n",
            "Epoch 2079/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 851516.8606 - reconstruction_loss: 13382.7705 - vqvae_loss: 838146.0505\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 851516.8606 - reconstruction_loss: 13382.7705 - vqvae_loss: 838146.0505\n",
            "Epoch 2080/2500\n",
            "Epoch 2080/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 819910.1166 - reconstruction_loss: 13331.2266 - vqvae_loss: 806685.5601\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 819910.1166 - reconstruction_loss: 13331.2266 - vqvae_loss: 806685.5601\n",
            "Epoch 2081/2500\n",
            "Epoch 2081/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 816364.0817 - reconstruction_loss: 13387.7227 - vqvae_loss: 802958.9724\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 816364.0817 - reconstruction_loss: 13387.7227 - vqvae_loss: 802958.9724\n",
            "Epoch 2082/2500\n",
            "Epoch 2082/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 812079.4159 - reconstruction_loss: 13366.5664 - vqvae_loss: 798721.6214\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 812079.4159 - reconstruction_loss: 13366.5664 - vqvae_loss: 798721.6214\n",
            "Epoch 2083/2500\n",
            "Epoch 2083/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 814381.6106 - reconstruction_loss: 13315.5244 - vqvae_loss: 801049.4880\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 814381.6106 - reconstruction_loss: 13315.5244 - vqvae_loss: 801049.4880\n",
            "Epoch 2084/2500\n",
            "Epoch 2084/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 802232.5337 - reconstruction_loss: 13327.4482 - vqvae_loss: 788929.1010\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 802232.5337 - reconstruction_loss: 13327.4482 - vqvae_loss: 788929.1010\n",
            "Epoch 2085/2500\n",
            "Epoch 2085/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 818910.2428 - reconstruction_loss: 13362.1846 - vqvae_loss: 805605.0841\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 818910.2428 - reconstruction_loss: 13362.1846 - vqvae_loss: 805605.0841\n",
            "Epoch 2086/2500\n",
            "Epoch 2086/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 830294.1070 - reconstruction_loss: 13343.5225 - vqvae_loss: 816956.9651\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 830294.1070 - reconstruction_loss: 13343.5225 - vqvae_loss: 816956.9651\n",
            "Epoch 2087/2500\n",
            "Epoch 2087/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 824757.8582 - reconstruction_loss: 13308.0879 - vqvae_loss: 811495.1334\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 824757.8582 - reconstruction_loss: 13308.0879 - vqvae_loss: 811495.1334\n",
            "Epoch 2088/2500\n",
            "Epoch 2088/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 815061.1803 - reconstruction_loss: 13356.7295 - vqvae_loss: 801727.6827\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 815061.1803 - reconstruction_loss: 13356.7295 - vqvae_loss: 801727.6827\n",
            "Epoch 2089/2500\n",
            "Epoch 2089/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 823696.9663 - reconstruction_loss: 13280.3496 - vqvae_loss: 810347.6791\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 823696.9663 - reconstruction_loss: 13280.3496 - vqvae_loss: 810347.6791\n",
            "Epoch 2090/2500\n",
            "Epoch 2090/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 820321.2981 - reconstruction_loss: 13271.4805 - vqvae_loss: 806976.4688\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 820321.2981 - reconstruction_loss: 13271.4805 - vqvae_loss: 806976.4688\n",
            "Epoch 2091/2500\n",
            "Epoch 2091/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 825573.2524 - reconstruction_loss: 13311.2822 - vqvae_loss: 812300.6971\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 825573.2524 - reconstruction_loss: 13311.2822 - vqvae_loss: 812300.6971\n",
            "Epoch 2092/2500\n",
            "Epoch 2092/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 804937.4315 - reconstruction_loss: 13287.8125 - vqvae_loss: 791625.1430\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 804937.4315 - reconstruction_loss: 13287.8125 - vqvae_loss: 791625.1430\n",
            "Epoch 2093/2500\n",
            "Epoch 2093/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 790581.2812 - reconstruction_loss: 13267.9697 - vqvae_loss: 777263.3425\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 790581.2812 - reconstruction_loss: 13267.9697 - vqvae_loss: 777263.3425\n",
            "Epoch 2094/2500\n",
            "Epoch 2094/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 800482.6106 - reconstruction_loss: 13256.8164 - vqvae_loss: 787192.2272\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 800482.6106 - reconstruction_loss: 13256.8164 - vqvae_loss: 787192.2272\n",
            "Epoch 2095/2500\n",
            "Epoch 2095/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 804040.1442 - reconstruction_loss: 13315.6650 - vqvae_loss: 790714.5781\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 804040.1442 - reconstruction_loss: 13315.6650 - vqvae_loss: 790714.5781\n",
            "Epoch 2096/2500\n",
            "Epoch 2096/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 797758.7284 - reconstruction_loss: 13269.4961 - vqvae_loss: 784536.7800\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 797758.7284 - reconstruction_loss: 13269.4961 - vqvae_loss: 784536.7800\n",
            "Epoch 2097/2500\n",
            "Epoch 2097/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 783378.4255 - reconstruction_loss: 13262.4238 - vqvae_loss: 770097.7127\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 783378.4255 - reconstruction_loss: 13262.4238 - vqvae_loss: 770097.7127\n",
            "Epoch 2098/2500\n",
            "Epoch 2098/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 797985.0445 - reconstruction_loss: 13275.7529 - vqvae_loss: 784689.7776\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 797985.0445 - reconstruction_loss: 13275.7529 - vqvae_loss: 784689.7776\n",
            "Epoch 2099/2500\n",
            "Epoch 2099/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 782508.8630 - reconstruction_loss: 13284.8740 - vqvae_loss: 769328.9856\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 782508.8630 - reconstruction_loss: 13284.8740 - vqvae_loss: 769328.9856\n",
            "Epoch 2100/2500\n",
            "Epoch 2100/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 790568.5829 - reconstruction_loss: 13283.2207 - vqvae_loss: 777361.4567\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 790568.5829 - reconstruction_loss: 13283.2207 - vqvae_loss: 777361.4567\n",
            "Epoch 2101/2500\n",
            "Epoch 2101/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 791080.3137 - reconstruction_loss: 13286.7646 - vqvae_loss: 777820.0036\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 791080.3137 - reconstruction_loss: 13286.7646 - vqvae_loss: 777820.0036\n",
            "Epoch 2102/2500\n",
            "Epoch 2102/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 793322.9651 - reconstruction_loss: 13290.9951 - vqvae_loss: 779895.7560\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 793322.9651 - reconstruction_loss: 13290.9951 - vqvae_loss: 779895.7560\n",
            "Epoch 2103/2500\n",
            "Epoch 2103/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 787519.0252 - reconstruction_loss: 13313.1982 - vqvae_loss: 774084.0889\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 787519.0252 - reconstruction_loss: 13313.1982 - vqvae_loss: 774084.0889\n",
            "Epoch 2104/2500\n",
            "Epoch 2104/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 773807.4675 - reconstruction_loss: 13245.5283 - vqvae_loss: 760630.8425\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 773807.4675 - reconstruction_loss: 13245.5283 - vqvae_loss: 760630.8425\n",
            "Epoch 2105/2500\n",
            "Epoch 2105/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 780558.5421 - reconstruction_loss: 13265.3037 - vqvae_loss: 767330.4700\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 780558.5421 - reconstruction_loss: 13265.3037 - vqvae_loss: 767330.4700\n",
            "Epoch 2106/2500\n",
            "Epoch 2106/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 768108.4579 - reconstruction_loss: 13246.6826 - vqvae_loss: 754922.4351\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 768108.4579 - reconstruction_loss: 13246.6826 - vqvae_loss: 754922.4351\n",
            "Epoch 2107/2500\n",
            "Epoch 2107/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 756985.2260 - reconstruction_loss: 13313.6914 - vqvae_loss: 743678.6611\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 756985.2260 - reconstruction_loss: 13313.6914 - vqvae_loss: 743678.6611\n",
            "Epoch 2108/2500\n",
            "Epoch 2108/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 774878.7248 - reconstruction_loss: 13268.1523 - vqvae_loss: 761566.3221\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 774878.7248 - reconstruction_loss: 13268.1523 - vqvae_loss: 761566.3221\n",
            "Epoch 2109/2500\n",
            "Epoch 2109/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760015.1623 - reconstruction_loss: 13311.9971 - vqvae_loss: 746767.0829\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760015.1623 - reconstruction_loss: 13311.9971 - vqvae_loss: 746767.0829\n",
            "Epoch 2110/2500\n",
            "Epoch 2110/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760737.3606 - reconstruction_loss: 13227.3838 - vqvae_loss: 747515.8462\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760737.3606 - reconstruction_loss: 13227.3838 - vqvae_loss: 747515.8462\n",
            "Epoch 2111/2500\n",
            "Epoch 2111/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 758658.5505 - reconstruction_loss: 13282.5703 - vqvae_loss: 745314.1226\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 758658.5505 - reconstruction_loss: 13282.5703 - vqvae_loss: 745314.1226\n",
            "Epoch 2112/2500\n",
            "Epoch 2112/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 768763.6647 - reconstruction_loss: 13567.4033 - vqvae_loss: 755302.5048\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 768763.6647 - reconstruction_loss: 13567.4033 - vqvae_loss: 755302.5048\n",
            "Epoch 2113/2500\n",
            "Epoch 2113/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 753635.5024 - reconstruction_loss: 13322.6299 - vqvae_loss: 740284.1887\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 753635.5024 - reconstruction_loss: 13322.6299 - vqvae_loss: 740284.1887\n",
            "Epoch 2114/2500\n",
            "Epoch 2114/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760573.5829 - reconstruction_loss: 13269.5684 - vqvae_loss: 747217.6538\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 760573.5829 - reconstruction_loss: 13269.5684 - vqvae_loss: 747217.6538\n",
            "Epoch 2115/2500\n",
            "Epoch 2115/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 755356.9724 - reconstruction_loss: 13320.3975 - vqvae_loss: 742003.8041\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 755356.9724 - reconstruction_loss: 13320.3975 - vqvae_loss: 742003.8041\n",
            "Epoch 2116/2500\n",
            "Epoch 2116/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770086.6995 - reconstruction_loss: 13271.2646 - vqvae_loss: 756723.9688\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770086.6995 - reconstruction_loss: 13271.2646 - vqvae_loss: 756723.9688\n",
            "Epoch 2117/2500\n",
            "Epoch 2117/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 748807.7188 - reconstruction_loss: 13304.7070 - vqvae_loss: 735568.4964\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 748807.7188 - reconstruction_loss: 13304.7070 - vqvae_loss: 735568.4964\n",
            "Epoch 2118/2500\n",
            "Epoch 2118/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 746879.9219 - reconstruction_loss: 13376.7061 - vqvae_loss: 733568.8762\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 746879.9219 - reconstruction_loss: 13376.7061 - vqvae_loss: 733568.8762\n",
            "Epoch 2119/2500\n",
            "Epoch 2119/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 748207.1298 - reconstruction_loss: 13189.7920 - vqvae_loss: 734951.4976\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 748207.1298 - reconstruction_loss: 13189.7920 - vqvae_loss: 734951.4976\n",
            "Epoch 2120/2500\n",
            "Epoch 2120/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 740555.5397 - reconstruction_loss: 13196.0537 - vqvae_loss: 727335.0264\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 740555.5397 - reconstruction_loss: 13196.0537 - vqvae_loss: 727335.0264\n",
            "Epoch 2121/2500\n",
            "Epoch 2121/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 740564.0337 - reconstruction_loss: 13204.4775 - vqvae_loss: 727326.1082\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 740564.0337 - reconstruction_loss: 13204.4775 - vqvae_loss: 727326.1082\n",
            "Epoch 2122/2500\n",
            "Epoch 2122/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 735249.9087 - reconstruction_loss: 13243.6191 - vqvae_loss: 721888.7740\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 735249.9087 - reconstruction_loss: 13243.6191 - vqvae_loss: 721888.7740\n",
            "Epoch 2123/2500\n",
            "Epoch 2123/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 737650.9255 - reconstruction_loss: 13177.9229 - vqvae_loss: 724337.2332\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 737650.9255 - reconstruction_loss: 13177.9229 - vqvae_loss: 724337.2332\n",
            "Epoch 2124/2500\n",
            "Epoch 2124/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 734766.8462 - reconstruction_loss: 13209.0684 - vqvae_loss: 721570.4327\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 734766.8462 - reconstruction_loss: 13209.0684 - vqvae_loss: 721570.4327\n",
            "Epoch 2125/2500\n",
            "Epoch 2125/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739196.8978 - reconstruction_loss: 13247.6201 - vqvae_loss: 725939.5781\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739196.8978 - reconstruction_loss: 13247.6201 - vqvae_loss: 725939.5781\n",
            "Epoch 2126/2500\n",
            "Epoch 2126/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 725772.8930 - reconstruction_loss: 13255.1396 - vqvae_loss: 712493.1478\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 725772.8930 - reconstruction_loss: 13255.1396 - vqvae_loss: 712493.1478\n",
            "Epoch 2127/2500\n",
            "Epoch 2127/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 729010.3474 - reconstruction_loss: 13249.4482 - vqvae_loss: 715791.8534\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 729010.3474 - reconstruction_loss: 13249.4482 - vqvae_loss: 715791.8534\n",
            "Epoch 2128/2500\n",
            "Epoch 2128/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 734805.2837 - reconstruction_loss: 13246.5342 - vqvae_loss: 721513.7716\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 734805.2837 - reconstruction_loss: 13246.5342 - vqvae_loss: 721513.7716\n",
            "Epoch 2129/2500\n",
            "Epoch 2129/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 735187.8137 - reconstruction_loss: 13192.6162 - vqvae_loss: 721867.6526\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 735187.8137 - reconstruction_loss: 13192.6162 - vqvae_loss: 721867.6526\n",
            "Epoch 2130/2500\n",
            "Epoch 2130/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 734773.3486 - reconstruction_loss: 13246.7627 - vqvae_loss: 721496.7404\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 734773.3486 - reconstruction_loss: 13246.7627 - vqvae_loss: 721496.7404\n",
            "Epoch 2131/2500\n",
            "Epoch 2131/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 735323.4002 - reconstruction_loss: 13388.7539 - vqvae_loss: 722057.0325\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 735323.4002 - reconstruction_loss: 13388.7539 - vqvae_loss: 722057.0325\n",
            "Epoch 2132/2500\n",
            "Epoch 2132/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 742978.2788 - reconstruction_loss: 13223.3252 - vqvae_loss: 729811.3978\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 742978.2788 - reconstruction_loss: 13223.3252 - vqvae_loss: 729811.3978\n",
            "Epoch 2133/2500\n",
            "Epoch 2133/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 733073.0733 - reconstruction_loss: 13214.3809 - vqvae_loss: 719853.8425\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 733073.0733 - reconstruction_loss: 13214.3809 - vqvae_loss: 719853.8425\n",
            "Epoch 2134/2500\n",
            "Epoch 2134/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 741887.3041 - reconstruction_loss: 13230.6221 - vqvae_loss: 728655.1827\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 741887.3041 - reconstruction_loss: 13230.6221 - vqvae_loss: 728655.1827\n",
            "Epoch 2135/2500\n",
            "Epoch 2135/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 739492.8425 - reconstruction_loss: 13337.1963 - vqvae_loss: 726119.0144\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 739492.8425 - reconstruction_loss: 13337.1963 - vqvae_loss: 726119.0144\n",
            "Epoch 2136/2500\n",
            "Epoch 2136/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739214.6538 - reconstruction_loss: 13208.3896 - vqvae_loss: 725938.5276\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739214.6538 - reconstruction_loss: 13208.3896 - vqvae_loss: 725938.5276\n",
            "Epoch 2137/2500\n",
            "Epoch 2137/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 733593.3462 - reconstruction_loss: 13181.8135 - vqvae_loss: 720437.4062\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 733593.3462 - reconstruction_loss: 13181.8135 - vqvae_loss: 720437.4062\n",
            "Epoch 2138/2500\n",
            "Epoch 2138/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 724327.1430 - reconstruction_loss: 13194.2246 - vqvae_loss: 711106.4231\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 724327.1430 - reconstruction_loss: 13194.2246 - vqvae_loss: 711106.4231\n",
            "Epoch 2139/2500\n",
            "Epoch 2139/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 736310.6154 - reconstruction_loss: 13195.2725 - vqvae_loss: 722985.4123\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 736310.6154 - reconstruction_loss: 13195.2725 - vqvae_loss: 722985.4123\n",
            "Epoch 2140/2500\n",
            "Epoch 2140/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 746106.7933 - reconstruction_loss: 13241.1299 - vqvae_loss: 732857.5805\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 746106.7933 - reconstruction_loss: 13241.1299 - vqvae_loss: 732857.5805\n",
            "Epoch 2141/2500\n",
            "Epoch 2141/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 746108.6947 - reconstruction_loss: 13222.2627 - vqvae_loss: 732896.0216\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 746108.6947 - reconstruction_loss: 13222.2627 - vqvae_loss: 732896.0216\n",
            "Epoch 2142/2500\n",
            "Epoch 2142/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 747183.9567 - reconstruction_loss: 13213.8750 - vqvae_loss: 734016.0048\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 747183.9567 - reconstruction_loss: 13213.8750 - vqvae_loss: 734016.0048\n",
            "Epoch 2143/2500\n",
            "Epoch 2143/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 745756.8329 - reconstruction_loss: 13147.7275 - vqvae_loss: 732607.5240\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 745756.8329 - reconstruction_loss: 13147.7275 - vqvae_loss: 732607.5240\n",
            "Epoch 2144/2500\n",
            "Epoch 2144/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 744173.1214 - reconstruction_loss: 13151.5586 - vqvae_loss: 731036.6851\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 744173.1214 - reconstruction_loss: 13151.5586 - vqvae_loss: 731036.6851\n",
            "Epoch 2145/2500\n",
            "Epoch 2145/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 736658.9627 - reconstruction_loss: 13172.7080 - vqvae_loss: 723571.0649\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 736658.9627 - reconstruction_loss: 13172.7080 - vqvae_loss: 723571.0649\n",
            "Epoch 2146/2500\n",
            "Epoch 2146/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 739050.3101 - reconstruction_loss: 13151.8975 - vqvae_loss: 725937.6695\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 739050.3101 - reconstruction_loss: 13151.8975 - vqvae_loss: 725937.6695\n",
            "Epoch 2147/2500\n",
            "Epoch 2147/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 735933.7380 - reconstruction_loss: 13212.8721 - vqvae_loss: 722618.5649\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 735933.7380 - reconstruction_loss: 13212.8721 - vqvae_loss: 722618.5649\n",
            "Epoch 2148/2500\n",
            "Epoch 2148/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 741551.2236 - reconstruction_loss: 13159.3857 - vqvae_loss: 728486.4615\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 741551.2236 - reconstruction_loss: 13159.3857 - vqvae_loss: 728486.4615\n",
            "Epoch 2149/2500\n",
            "Epoch 2149/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 772304.4447 - reconstruction_loss: 14264.7559 - vqvae_loss: 758461.0120\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 772304.4447 - reconstruction_loss: 14264.7559 - vqvae_loss: 758461.0120\n",
            "Epoch 2150/2500\n",
            "Epoch 2150/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 710390.2957 - reconstruction_loss: 13674.7100 - vqvae_loss: 696430.2344\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 710390.2957 - reconstruction_loss: 13674.7100 - vqvae_loss: 696430.2344\n",
            "Epoch 2151/2500\n",
            "Epoch 2151/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 716763.1526 - reconstruction_loss: 13336.0127 - vqvae_loss: 703373.4772\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 716763.1526 - reconstruction_loss: 13336.0127 - vqvae_loss: 703373.4772\n",
            "Epoch 2152/2500\n",
            "Epoch 2152/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 716694.6851 - reconstruction_loss: 13205.5137 - vqvae_loss: 703480.3269\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 716694.6851 - reconstruction_loss: 13205.5137 - vqvae_loss: 703480.3269\n",
            "Epoch 2153/2500\n",
            "Epoch 2153/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 729313.4543 - reconstruction_loss: 13139.6357 - vqvae_loss: 716141.3041\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 729313.4543 - reconstruction_loss: 13139.6357 - vqvae_loss: 716141.3041\n",
            "Epoch 2154/2500\n",
            "Epoch 2154/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 734023.3654 - reconstruction_loss: 13118.8271 - vqvae_loss: 720860.2151\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 734023.3654 - reconstruction_loss: 13118.8271 - vqvae_loss: 720860.2151\n",
            "Epoch 2155/2500\n",
            "Epoch 2155/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 729015.1250 - reconstruction_loss: 13131.7803 - vqvae_loss: 715890.9784\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 729015.1250 - reconstruction_loss: 13131.7803 - vqvae_loss: 715890.9784\n",
            "Epoch 2156/2500\n",
            "Epoch 2156/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 735278.6142 - reconstruction_loss: 13079.2422 - vqvae_loss: 722172.8594\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 735278.6142 - reconstruction_loss: 13079.2422 - vqvae_loss: 722172.8594\n",
            "Epoch 2157/2500\n",
            "Epoch 2157/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 738405.2308 - reconstruction_loss: 13168.5430 - vqvae_loss: 725303.3137\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 738405.2308 - reconstruction_loss: 13168.5430 - vqvae_loss: 725303.3137\n",
            "Epoch 2158/2500\n",
            "Epoch 2158/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 736946.4555 - reconstruction_loss: 13165.1055 - vqvae_loss: 723841.9495\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 736946.4555 - reconstruction_loss: 13165.1055 - vqvae_loss: 723841.9495\n",
            "Epoch 2159/2500\n",
            "Epoch 2159/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 744898.7380 - reconstruction_loss: 13094.8350 - vqvae_loss: 731749.8798\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 744898.7380 - reconstruction_loss: 13094.8350 - vqvae_loss: 731749.8798\n",
            "Epoch 2160/2500\n",
            "Epoch 2160/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739296.1502 - reconstruction_loss: 13064.0146 - vqvae_loss: 726285.7368\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 739296.1502 - reconstruction_loss: 13064.0146 - vqvae_loss: 726285.7368\n",
            "Epoch 2161/2500\n",
            "Epoch 2161/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 749611.2091 - reconstruction_loss: 13096.6738 - vqvae_loss: 736480.9748\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 749611.2091 - reconstruction_loss: 13096.6738 - vqvae_loss: 736480.9748\n",
            "Epoch 2162/2500\n",
            "Epoch 2162/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 755189.6514 - reconstruction_loss: 13138.7412 - vqvae_loss: 742038.7392\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 755189.6514 - reconstruction_loss: 13138.7412 - vqvae_loss: 742038.7392\n",
            "Epoch 2163/2500\n",
            "Epoch 2163/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 752373.7091 - reconstruction_loss: 13085.4170 - vqvae_loss: 739360.3666\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 752373.7091 - reconstruction_loss: 13085.4170 - vqvae_loss: 739360.3666\n",
            "Epoch 2164/2500\n",
            "Epoch 2164/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 769493.5000 - reconstruction_loss: 13106.4805 - vqvae_loss: 756508.4567\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 769493.5000 - reconstruction_loss: 13106.4805 - vqvae_loss: 756508.4567\n",
            "Epoch 2165/2500\n",
            "Epoch 2165/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771901.8125 - reconstruction_loss: 13116.6250 - vqvae_loss: 758808.6118\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771901.8125 - reconstruction_loss: 13116.6250 - vqvae_loss: 758808.6118\n",
            "Epoch 2166/2500\n",
            "Epoch 2166/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776789.5805 - reconstruction_loss: 13190.4150 - vqvae_loss: 763650.0060\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776789.5805 - reconstruction_loss: 13190.4150 - vqvae_loss: 763650.0060\n",
            "Epoch 2167/2500\n",
            "Epoch 2167/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 775419.5673 - reconstruction_loss: 13239.2119 - vqvae_loss: 762174.3606\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 775419.5673 - reconstruction_loss: 13239.2119 - vqvae_loss: 762174.3606\n",
            "Epoch 2168/2500\n",
            "Epoch 2168/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 781944.3714 - reconstruction_loss: 13210.8271 - vqvae_loss: 768716.3822\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 781944.3714 - reconstruction_loss: 13210.8271 - vqvae_loss: 768716.3822\n",
            "Epoch 2169/2500\n",
            "Epoch 2169/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 789227.8594 - reconstruction_loss: 13178.3799 - vqvae_loss: 776126.5481\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 789227.8594 - reconstruction_loss: 13178.3799 - vqvae_loss: 776126.5481\n",
            "Epoch 2170/2500\n",
            "Epoch 2170/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 793419.0805 - reconstruction_loss: 13153.4854 - vqvae_loss: 780250.8305\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 793419.0805 - reconstruction_loss: 13153.4854 - vqvae_loss: 780250.8305\n",
            "Epoch 2171/2500\n",
            "Epoch 2171/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 791465.8594 - reconstruction_loss: 13113.4102 - vqvae_loss: 778352.6719\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 791465.8594 - reconstruction_loss: 13113.4102 - vqvae_loss: 778352.6719\n",
            "Epoch 2172/2500\n",
            "Epoch 2172/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 797094.2825 - reconstruction_loss: 13069.2412 - vqvae_loss: 784004.5168\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 797094.2825 - reconstruction_loss: 13069.2412 - vqvae_loss: 784004.5168\n",
            "Epoch 2173/2500\n",
            "Epoch 2173/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 799363.7680 - reconstruction_loss: 13100.6143 - vqvae_loss: 786240.7452\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 799363.7680 - reconstruction_loss: 13100.6143 - vqvae_loss: 786240.7452\n",
            "Epoch 2174/2500\n",
            "Epoch 2174/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 796082.7596 - reconstruction_loss: 13170.2930 - vqvae_loss: 783056.5204\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 796082.7596 - reconstruction_loss: 13170.2930 - vqvae_loss: 783056.5204\n",
            "Epoch 2175/2500\n",
            "Epoch 2175/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800695.5998 - reconstruction_loss: 13143.2354 - vqvae_loss: 787630.6130\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 800695.5998 - reconstruction_loss: 13143.2354 - vqvae_loss: 787630.6130\n",
            "Epoch 2176/2500\n",
            "Epoch 2176/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 808744.2849 - reconstruction_loss: 13162.4170 - vqvae_loss: 795498.2788\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 808744.2849 - reconstruction_loss: 13162.4170 - vqvae_loss: 795498.2788\n",
            "Epoch 2177/2500\n",
            "Epoch 2177/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808501.5108 - reconstruction_loss: 13112.6650 - vqvae_loss: 795366.3413\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 808501.5108 - reconstruction_loss: 13112.6650 - vqvae_loss: 795366.3413\n",
            "Epoch 2178/2500\n",
            "Epoch 2178/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 800406.0457 - reconstruction_loss: 13137.1533 - vqvae_loss: 787274.3401\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 800406.0457 - reconstruction_loss: 13137.1533 - vqvae_loss: 787274.3401\n",
            "Epoch 2179/2500\n",
            "Epoch 2179/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 805484.2115 - reconstruction_loss: 13118.4092 - vqvae_loss: 792353.3377\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 805484.2115 - reconstruction_loss: 13118.4092 - vqvae_loss: 792353.3377\n",
            "Epoch 2180/2500\n",
            "Epoch 2180/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 797990.5829 - reconstruction_loss: 13145.9912 - vqvae_loss: 784822.9483\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 797990.5829 - reconstruction_loss: 13145.9912 - vqvae_loss: 784822.9483\n",
            "Epoch 2181/2500\n",
            "Epoch 2181/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 801258.2500 - reconstruction_loss: 13125.6973 - vqvae_loss: 788045.0685\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 801258.2500 - reconstruction_loss: 13125.6973 - vqvae_loss: 788045.0685\n",
            "Epoch 2182/2500\n",
            "Epoch 2182/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 795825.5108 - reconstruction_loss: 13107.1816 - vqvae_loss: 782668.2248\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 795825.5108 - reconstruction_loss: 13107.1816 - vqvae_loss: 782668.2248\n",
            "Epoch 2183/2500\n",
            "Epoch 2183/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 794753.0252 - reconstruction_loss: 13105.1689 - vqvae_loss: 781643.1466\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 794753.0252 - reconstruction_loss: 13105.1689 - vqvae_loss: 781643.1466\n",
            "Epoch 2184/2500\n",
            "Epoch 2184/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 788184.3089 - reconstruction_loss: 13150.1914 - vqvae_loss: 775048.6695\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 788184.3089 - reconstruction_loss: 13150.1914 - vqvae_loss: 775048.6695\n",
            "Epoch 2185/2500\n",
            "Epoch 2185/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 799154.9062 - reconstruction_loss: 13175.1904 - vqvae_loss: 785970.7788\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 799154.9062 - reconstruction_loss: 13175.1904 - vqvae_loss: 785970.7788\n",
            "Epoch 2186/2500\n",
            "Epoch 2186/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 794355.7680 - reconstruction_loss: 13097.1924 - vqvae_loss: 781205.6322\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 794355.7680 - reconstruction_loss: 13097.1924 - vqvae_loss: 781205.6322\n",
            "Epoch 2187/2500\n",
            "Epoch 2187/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 792528.6046 - reconstruction_loss: 13138.7461 - vqvae_loss: 779350.6286\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 792528.6046 - reconstruction_loss: 13138.7461 - vqvae_loss: 779350.6286\n",
            "Epoch 2188/2500\n",
            "Epoch 2188/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 794776.0757 - reconstruction_loss: 13137.1055 - vqvae_loss: 781598.7969\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 794776.0757 - reconstruction_loss: 13137.1055 - vqvae_loss: 781598.7969\n",
            "Epoch 2189/2500\n",
            "Epoch 2189/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 792309.0974 - reconstruction_loss: 13156.8252 - vqvae_loss: 779112.7740\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 792309.0974 - reconstruction_loss: 13156.8252 - vqvae_loss: 779112.7740\n",
            "Epoch 2190/2500\n",
            "Epoch 2190/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 787578.9820 - reconstruction_loss: 13116.3604 - vqvae_loss: 774510.5781\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 787578.9820 - reconstruction_loss: 13116.3604 - vqvae_loss: 774510.5781\n",
            "Epoch 2191/2500\n",
            "Epoch 2191/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 795032.5889 - reconstruction_loss: 13109.9336 - vqvae_loss: 781916.1647\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 795032.5889 - reconstruction_loss: 13109.9336 - vqvae_loss: 781916.1647\n",
            "Epoch 2192/2500\n",
            "Epoch 2192/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 798010.1262 - reconstruction_loss: 13163.9268 - vqvae_loss: 784862.5986\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 798010.1262 - reconstruction_loss: 13163.9268 - vqvae_loss: 784862.5986\n",
            "Epoch 2193/2500\n",
            "Epoch 2193/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 798759.8425 - reconstruction_loss: 13111.0615 - vqvae_loss: 785623.3534\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 798759.8425 - reconstruction_loss: 13111.0615 - vqvae_loss: 785623.3534\n",
            "Epoch 2194/2500\n",
            "Epoch 2194/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 802031.4014 - reconstruction_loss: 13144.7949 - vqvae_loss: 788760.0216\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 802031.4014 - reconstruction_loss: 13144.7949 - vqvae_loss: 788760.0216\n",
            "Epoch 2195/2500\n",
            "Epoch 2195/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 791566.8714 - reconstruction_loss: 13144.4043 - vqvae_loss: 778365.6418\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 791566.8714 - reconstruction_loss: 13144.4043 - vqvae_loss: 778365.6418\n",
            "Epoch 2196/2500\n",
            "Epoch 2196/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 793827.5673 - reconstruction_loss: 13156.4219 - vqvae_loss: 780752.2428\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 793827.5673 - reconstruction_loss: 13156.4219 - vqvae_loss: 780752.2428\n",
            "Epoch 2197/2500\n",
            "Epoch 2197/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798819.7837 - reconstruction_loss: 13152.5195 - vqvae_loss: 785599.8005\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 798819.7837 - reconstruction_loss: 13152.5195 - vqvae_loss: 785599.8005\n",
            "Epoch 2198/2500\n",
            "Epoch 2198/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812838.4183 - reconstruction_loss: 13120.7197 - vqvae_loss: 799665.6322\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 812838.4183 - reconstruction_loss: 13120.7197 - vqvae_loss: 799665.6322\n",
            "Epoch 2199/2500\n",
            "Epoch 2199/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 809912.5962 - reconstruction_loss: 13172.1973 - vqvae_loss: 796757.7272\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 809912.5962 - reconstruction_loss: 13172.1973 - vqvae_loss: 796757.7272\n",
            "Epoch 2200/2500\n",
            "Epoch 2200/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 811659.4291 - reconstruction_loss: 13185.2158 - vqvae_loss: 798369.2957\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 811659.4291 - reconstruction_loss: 13185.2158 - vqvae_loss: 798369.2957\n",
            "Epoch 2201/2500\n",
            "Epoch 2201/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808055.2404 - reconstruction_loss: 13122.1123 - vqvae_loss: 794940.8413\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808055.2404 - reconstruction_loss: 13122.1123 - vqvae_loss: 794940.8413\n",
            "Epoch 2202/2500\n",
            "Epoch 2202/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 804957.7560 - reconstruction_loss: 13120.3994 - vqvae_loss: 791844.0962\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 804957.7560 - reconstruction_loss: 13120.3994 - vqvae_loss: 791844.0962\n",
            "Epoch 2203/2500\n",
            "Epoch 2203/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 810743.0120 - reconstruction_loss: 13133.5088 - vqvae_loss: 797592.2284\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 810743.0120 - reconstruction_loss: 13133.5088 - vqvae_loss: 797592.2284\n",
            "Epoch 2204/2500\n",
            "Epoch 2204/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 812017.4255 - reconstruction_loss: 13140.2842 - vqvae_loss: 798948.1298\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 812017.4255 - reconstruction_loss: 13140.2842 - vqvae_loss: 798948.1298\n",
            "Epoch 2205/2500\n",
            "Epoch 2205/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 818814.2260 - reconstruction_loss: 13114.3037 - vqvae_loss: 805738.3450\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 818814.2260 - reconstruction_loss: 13114.3037 - vqvae_loss: 805738.3450\n",
            "Epoch 2206/2500\n",
            "Epoch 2206/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824989.8389 - reconstruction_loss: 13144.3740 - vqvae_loss: 811801.0793\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 824989.8389 - reconstruction_loss: 13144.3740 - vqvae_loss: 811801.0793\n",
            "Epoch 2207/2500\n",
            "Epoch 2207/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 818788.7921 - reconstruction_loss: 13104.9482 - vqvae_loss: 805730.5781\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 818788.7921 - reconstruction_loss: 13104.9482 - vqvae_loss: 805730.5781\n",
            "Epoch 2208/2500\n",
            "Epoch 2208/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 817552.9387 - reconstruction_loss: 13120.2354 - vqvae_loss: 804350.5361\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 817552.9387 - reconstruction_loss: 13120.2354 - vqvae_loss: 804350.5361\n",
            "Epoch 2209/2500\n",
            "Epoch 2209/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 811619.3966 - reconstruction_loss: 13124.5146 - vqvae_loss: 798448.8149\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 811619.3966 - reconstruction_loss: 13124.5146 - vqvae_loss: 798448.8149\n",
            "Epoch 2210/2500\n",
            "Epoch 2210/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808633.8558 - reconstruction_loss: 13096.5752 - vqvae_loss: 795598.8281\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808633.8558 - reconstruction_loss: 13096.5752 - vqvae_loss: 795598.8281\n",
            "Epoch 2211/2500\n",
            "Epoch 2211/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819647.5276 - reconstruction_loss: 13086.5293 - vqvae_loss: 806432.7957\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 819647.5276 - reconstruction_loss: 13086.5293 - vqvae_loss: 806432.7957\n",
            "Epoch 2212/2500\n",
            "Epoch 2212/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 822382.6070 - reconstruction_loss: 13137.7686 - vqvae_loss: 809135.4339\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 822382.6070 - reconstruction_loss: 13137.7686 - vqvae_loss: 809135.4339\n",
            "Epoch 2213/2500\n",
            "Epoch 2213/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 811960.7175 - reconstruction_loss: 13105.2070 - vqvae_loss: 798890.2560\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 811960.7175 - reconstruction_loss: 13105.2070 - vqvae_loss: 798890.2560\n",
            "Epoch 2214/2500\n",
            "Epoch 2214/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808854.3654 - reconstruction_loss: 13101.3213 - vqvae_loss: 795729.7837\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 808854.3654 - reconstruction_loss: 13101.3213 - vqvae_loss: 795729.7837\n",
            "Epoch 2215/2500\n",
            "Epoch 2215/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 806363.5481 - reconstruction_loss: 13085.8906 - vqvae_loss: 793226.6214\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 806363.5481 - reconstruction_loss: 13085.8906 - vqvae_loss: 793226.6214\n",
            "Epoch 2216/2500\n",
            "Epoch 2216/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 802493.7668 - reconstruction_loss: 13098.2227 - vqvae_loss: 789454.0457\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 802493.7668 - reconstruction_loss: 13098.2227 - vqvae_loss: 789454.0457\n",
            "Epoch 2217/2500\n",
            "Epoch 2217/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 798696.7800 - reconstruction_loss: 13066.4385 - vqvae_loss: 785696.1514\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 798696.7800 - reconstruction_loss: 13066.4385 - vqvae_loss: 785696.1514\n",
            "Epoch 2218/2500\n",
            "Epoch 2218/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 798551.7536 - reconstruction_loss: 13106.7471 - vqvae_loss: 785424.6947\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 798551.7536 - reconstruction_loss: 13106.7471 - vqvae_loss: 785424.6947\n",
            "Epoch 2219/2500\n",
            "Epoch 2219/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 801927.0240 - reconstruction_loss: 13149.3789 - vqvae_loss: 788784.4423\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 801927.0240 - reconstruction_loss: 13149.3789 - vqvae_loss: 788784.4423\n",
            "Epoch 2220/2500\n",
            "Epoch 2220/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 794307.6707 - reconstruction_loss: 13102.1621 - vqvae_loss: 781244.0469\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 794307.6707 - reconstruction_loss: 13102.1621 - vqvae_loss: 781244.0469\n",
            "Epoch 2221/2500\n",
            "Epoch 2221/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 787866.9183 - reconstruction_loss: 13058.4893 - vqvae_loss: 774835.4892\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 787866.9183 - reconstruction_loss: 13058.4893 - vqvae_loss: 774835.4892\n",
            "Epoch 2222/2500\n",
            "Epoch 2222/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 792587.0481 - reconstruction_loss: 13082.8359 - vqvae_loss: 779483.9663\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 792587.0481 - reconstruction_loss: 13082.8359 - vqvae_loss: 779483.9663\n",
            "Epoch 2223/2500\n",
            "Epoch 2223/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784227.4543 - reconstruction_loss: 13064.9805 - vqvae_loss: 771195.3990\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784227.4543 - reconstruction_loss: 13064.9805 - vqvae_loss: 771195.3990\n",
            "Epoch 2224/2500\n",
            "Epoch 2224/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 779577.3377 - reconstruction_loss: 13071.8691 - vqvae_loss: 766586.2752\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 779577.3377 - reconstruction_loss: 13071.8691 - vqvae_loss: 766586.2752\n",
            "Epoch 2225/2500\n",
            "Epoch 2225/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 779065.0541 - reconstruction_loss: 13065.7314 - vqvae_loss: 766016.0108\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 779065.0541 - reconstruction_loss: 13065.7314 - vqvae_loss: 766016.0108\n",
            "Epoch 2226/2500\n",
            "Epoch 2226/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 778708.5709 - reconstruction_loss: 13028.6865 - vqvae_loss: 765731.5986\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 778708.5709 - reconstruction_loss: 13028.6865 - vqvae_loss: 765731.5986\n",
            "Epoch 2227/2500\n",
            "Epoch 2227/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 775998.2945 - reconstruction_loss: 13037.3047 - vqvae_loss: 762940.9639\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 775998.2945 - reconstruction_loss: 13037.3047 - vqvae_loss: 762940.9639\n",
            "Epoch 2228/2500\n",
            "Epoch 2228/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776085.5433 - reconstruction_loss: 13045.4619 - vqvae_loss: 763013.4555\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776085.5433 - reconstruction_loss: 13045.4619 - vqvae_loss: 763013.4555\n",
            "Epoch 2229/2500\n",
            "Epoch 2229/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772920.4904 - reconstruction_loss: 13085.4922 - vqvae_loss: 759863.2224\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772920.4904 - reconstruction_loss: 13085.4922 - vqvae_loss: 759863.2224\n",
            "Epoch 2230/2500\n",
            "Epoch 2230/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 776990.7007 - reconstruction_loss: 13087.2930 - vqvae_loss: 763925.5745\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 776990.7007 - reconstruction_loss: 13087.2930 - vqvae_loss: 763925.5745\n",
            "Epoch 2231/2500\n",
            "Epoch 2231/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 774281.8450 - reconstruction_loss: 13084.0049 - vqvae_loss: 761227.6526\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 774281.8450 - reconstruction_loss: 13084.0049 - vqvae_loss: 761227.6526\n",
            "Epoch 2232/2500\n",
            "Epoch 2232/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 772877.4615 - reconstruction_loss: 13036.4766 - vqvae_loss: 759794.4892\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 772877.4615 - reconstruction_loss: 13036.4766 - vqvae_loss: 759794.4892\n",
            "Epoch 2233/2500\n",
            "Epoch 2233/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 768900.9868 - reconstruction_loss: 13004.5430 - vqvae_loss: 755875.5204\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 768900.9868 - reconstruction_loss: 13004.5430 - vqvae_loss: 755875.5204\n",
            "Epoch 2234/2500\n",
            "Epoch 2234/2500\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 761058.0180 - reconstruction_loss: 12986.5977 - vqvae_loss: 748110.3305\n",
            "51/51 [==============================] - 5s 100ms/step - loss: 761058.0180 - reconstruction_loss: 12986.5977 - vqvae_loss: 748110.3305\n",
            "Epoch 2235/2500\n",
            "Epoch 2235/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 757498.5493 - reconstruction_loss: 13048.0752 - vqvae_loss: 744470.0300\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 757498.5493 - reconstruction_loss: 13048.0752 - vqvae_loss: 744470.0300\n",
            "Epoch 2236/2500\n",
            "Epoch 2236/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 758891.2620 - reconstruction_loss: 13036.5010 - vqvae_loss: 745812.1791\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 758891.2620 - reconstruction_loss: 13036.5010 - vqvae_loss: 745812.1791\n",
            "Epoch 2237/2500\n",
            "Epoch 2237/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 755450.8269 - reconstruction_loss: 13071.4727 - vqvae_loss: 742331.9159\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 755450.8269 - reconstruction_loss: 13071.4727 - vqvae_loss: 742331.9159\n",
            "Epoch 2238/2500\n",
            "Epoch 2238/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 762519.1719 - reconstruction_loss: 13043.6123 - vqvae_loss: 749428.5950\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 762519.1719 - reconstruction_loss: 13043.6123 - vqvae_loss: 749428.5950\n",
            "Epoch 2239/2500\n",
            "Epoch 2239/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 754839.7945 - reconstruction_loss: 13053.6211 - vqvae_loss: 741791.3654\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 754839.7945 - reconstruction_loss: 13053.6211 - vqvae_loss: 741791.3654\n",
            "Epoch 2240/2500\n",
            "Epoch 2240/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 750489.2308 - reconstruction_loss: 13085.3672 - vqvae_loss: 737427.5228\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 750489.2308 - reconstruction_loss: 13085.3672 - vqvae_loss: 737427.5228\n",
            "Epoch 2241/2500\n",
            "Epoch 2241/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 757809.0493 - reconstruction_loss: 13122.7402 - vqvae_loss: 744636.8630\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 757809.0493 - reconstruction_loss: 13122.7402 - vqvae_loss: 744636.8630\n",
            "Epoch 2242/2500\n",
            "Epoch 2242/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 754699.3618 - reconstruction_loss: 13046.8975 - vqvae_loss: 741663.5529\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 754699.3618 - reconstruction_loss: 13046.8975 - vqvae_loss: 741663.5529\n",
            "Epoch 2243/2500\n",
            "Epoch 2243/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 753792.3606 - reconstruction_loss: 13068.1162 - vqvae_loss: 740746.9651\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 753792.3606 - reconstruction_loss: 13068.1162 - vqvae_loss: 740746.9651\n",
            "Epoch 2244/2500\n",
            "Epoch 2244/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753440.6178 - reconstruction_loss: 13057.1719 - vqvae_loss: 740399.4159\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753440.6178 - reconstruction_loss: 13057.1719 - vqvae_loss: 740399.4159\n",
            "Epoch 2245/2500\n",
            "Epoch 2245/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 758405.5337 - reconstruction_loss: 13103.6426 - vqvae_loss: 745363.1875\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 758405.5337 - reconstruction_loss: 13103.6426 - vqvae_loss: 745363.1875\n",
            "Epoch 2246/2500\n",
            "Epoch 2246/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 756872.3401 - reconstruction_loss: 13082.0830 - vqvae_loss: 743676.9579\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 756872.3401 - reconstruction_loss: 13082.0830 - vqvae_loss: 743676.9579\n",
            "Epoch 2247/2500\n",
            "Epoch 2247/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 754879.5925 - reconstruction_loss: 13093.1582 - vqvae_loss: 741800.1082\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 754879.5925 - reconstruction_loss: 13093.1582 - vqvae_loss: 741800.1082\n",
            "Epoch 2248/2500\n",
            "Epoch 2248/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 754797.8678 - reconstruction_loss: 13109.5781 - vqvae_loss: 741788.0601\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 754797.8678 - reconstruction_loss: 13109.5781 - vqvae_loss: 741788.0601\n",
            "Epoch 2249/2500\n",
            "Epoch 2249/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 763140.8690 - reconstruction_loss: 13099.0723 - vqvae_loss: 750056.6262\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 763140.8690 - reconstruction_loss: 13099.0723 - vqvae_loss: 750056.6262\n",
            "Epoch 2250/2500\n",
            "Epoch 2250/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 758385.1010 - reconstruction_loss: 13063.5322 - vqvae_loss: 745317.3089\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 758385.1010 - reconstruction_loss: 13063.5322 - vqvae_loss: 745317.3089\n",
            "Epoch 2251/2500\n",
            "Epoch 2251/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 767818.1142 - reconstruction_loss: 13092.2461 - vqvae_loss: 754634.7981\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 767818.1142 - reconstruction_loss: 13092.2461 - vqvae_loss: 754634.7981\n",
            "Epoch 2252/2500\n",
            "Epoch 2252/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 751689.3137 - reconstruction_loss: 13095.4580 - vqvae_loss: 738688.9639\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 751689.3137 - reconstruction_loss: 13095.4580 - vqvae_loss: 738688.9639\n",
            "Epoch 2253/2500\n",
            "Epoch 2253/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 753986.4639 - reconstruction_loss: 13114.1729 - vqvae_loss: 740858.0565\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 753986.4639 - reconstruction_loss: 13114.1729 - vqvae_loss: 740858.0565\n",
            "Epoch 2254/2500\n",
            "Epoch 2254/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 755764.9026 - reconstruction_loss: 13074.0723 - vqvae_loss: 742614.4615\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 755764.9026 - reconstruction_loss: 13074.0723 - vqvae_loss: 742614.4615\n",
            "Epoch 2255/2500\n",
            "Epoch 2255/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 755237.6562 - reconstruction_loss: 13142.9326 - vqvae_loss: 742124.9868\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 755237.6562 - reconstruction_loss: 13142.9326 - vqvae_loss: 742124.9868\n",
            "Epoch 2256/2500\n",
            "Epoch 2256/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 753698.8534 - reconstruction_loss: 13121.3838 - vqvae_loss: 740568.0132\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 753698.8534 - reconstruction_loss: 13121.3838 - vqvae_loss: 740568.0132\n",
            "Epoch 2257/2500\n",
            "Epoch 2257/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 750238.5252 - reconstruction_loss: 13081.0283 - vqvae_loss: 737202.7296\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 750238.5252 - reconstruction_loss: 13081.0283 - vqvae_loss: 737202.7296\n",
            "Epoch 2258/2500\n",
            "Epoch 2258/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 750160.2680 - reconstruction_loss: 13088.8281 - vqvae_loss: 737017.2488\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 750160.2680 - reconstruction_loss: 13088.8281 - vqvae_loss: 737017.2488\n",
            "Epoch 2259/2500\n",
            "Epoch 2259/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 756926.8377 - reconstruction_loss: 13118.5195 - vqvae_loss: 743698.8678\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 756926.8377 - reconstruction_loss: 13118.5195 - vqvae_loss: 743698.8678\n",
            "Epoch 2260/2500\n",
            "Epoch 2260/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753148.8209 - reconstruction_loss: 13131.7920 - vqvae_loss: 739991.5385\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753148.8209 - reconstruction_loss: 13131.7920 - vqvae_loss: 739991.5385\n",
            "Epoch 2261/2500\n",
            "Epoch 2261/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753042.4832 - reconstruction_loss: 13116.9814 - vqvae_loss: 739947.8486\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753042.4832 - reconstruction_loss: 13116.9814 - vqvae_loss: 739947.8486\n",
            "Epoch 2262/2500\n",
            "Epoch 2262/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 752797.8546 - reconstruction_loss: 13076.1885 - vqvae_loss: 739736.9639\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 752797.8546 - reconstruction_loss: 13076.1885 - vqvae_loss: 739736.9639\n",
            "Epoch 2263/2500\n",
            "Epoch 2263/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 751268.4579 - reconstruction_loss: 13088.3926 - vqvae_loss: 738166.9700\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 751268.4579 - reconstruction_loss: 13088.3926 - vqvae_loss: 738166.9700\n",
            "Epoch 2264/2500\n",
            "Epoch 2264/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 748467.2584 - reconstruction_loss: 13112.6113 - vqvae_loss: 735292.0048\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 748467.2584 - reconstruction_loss: 13112.6113 - vqvae_loss: 735292.0048\n",
            "Epoch 2265/2500\n",
            "Epoch 2265/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 752555.9748 - reconstruction_loss: 13097.6719 - vqvae_loss: 739445.6839\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 752555.9748 - reconstruction_loss: 13097.6719 - vqvae_loss: 739445.6839\n",
            "Epoch 2266/2500\n",
            "Epoch 2266/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 751269.5577 - reconstruction_loss: 13074.2734 - vqvae_loss: 738163.3546\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 751269.5577 - reconstruction_loss: 13074.2734 - vqvae_loss: 738163.3546\n",
            "Epoch 2267/2500\n",
            "Epoch 2267/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 762030.9351 - reconstruction_loss: 13075.0586 - vqvae_loss: 748941.1034\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 762030.9351 - reconstruction_loss: 13075.0586 - vqvae_loss: 748941.1034\n",
            "Epoch 2268/2500\n",
            "Epoch 2268/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 760884.7428 - reconstruction_loss: 13074.4678 - vqvae_loss: 747882.8558\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 760884.7428 - reconstruction_loss: 13074.4678 - vqvae_loss: 747882.8558\n",
            "Epoch 2269/2500\n",
            "Epoch 2269/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 764387.1094 - reconstruction_loss: 13104.6719 - vqvae_loss: 751261.7356\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 764387.1094 - reconstruction_loss: 13104.6719 - vqvae_loss: 751261.7356\n",
            "Epoch 2270/2500\n",
            "Epoch 2270/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 760684.4639 - reconstruction_loss: 13087.1982 - vqvae_loss: 747647.5349\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 760684.4639 - reconstruction_loss: 13087.1982 - vqvae_loss: 747647.5349\n",
            "Epoch 2271/2500\n",
            "Epoch 2271/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 763541.8942 - reconstruction_loss: 13122.9521 - vqvae_loss: 750398.9038\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 763541.8942 - reconstruction_loss: 13122.9521 - vqvae_loss: 750398.9038\n",
            "Epoch 2272/2500\n",
            "Epoch 2272/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771091.9327 - reconstruction_loss: 13184.0137 - vqvae_loss: 757826.2139\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771091.9327 - reconstruction_loss: 13184.0137 - vqvae_loss: 757826.2139\n",
            "Epoch 2273/2500\n",
            "Epoch 2273/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 768266.4050 - reconstruction_loss: 13199.3086 - vqvae_loss: 755103.8726\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 768266.4050 - reconstruction_loss: 13199.3086 - vqvae_loss: 755103.8726\n",
            "Epoch 2274/2500\n",
            "Epoch 2274/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 772136.5421 - reconstruction_loss: 13085.4814 - vqvae_loss: 759048.1358\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 772136.5421 - reconstruction_loss: 13085.4814 - vqvae_loss: 759048.1358\n",
            "Epoch 2275/2500\n",
            "Epoch 2275/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775643.7332 - reconstruction_loss: 13052.0469 - vqvae_loss: 762404.1899\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775643.7332 - reconstruction_loss: 13052.0469 - vqvae_loss: 762404.1899\n",
            "Epoch 2276/2500\n",
            "Epoch 2276/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771417.1659 - reconstruction_loss: 13069.6680 - vqvae_loss: 758361.9171\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771417.1659 - reconstruction_loss: 13069.6680 - vqvae_loss: 758361.9171\n",
            "Epoch 2277/2500\n",
            "Epoch 2277/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 772870.2296 - reconstruction_loss: 13043.3076 - vqvae_loss: 759741.3197\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 772870.2296 - reconstruction_loss: 13043.3076 - vqvae_loss: 759741.3197\n",
            "Epoch 2278/2500\n",
            "Epoch 2278/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 770857.3173 - reconstruction_loss: 13083.6064 - vqvae_loss: 757758.0541\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 770857.3173 - reconstruction_loss: 13083.6064 - vqvae_loss: 757758.0541\n",
            "Epoch 2279/2500\n",
            "Epoch 2279/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771693.1142 - reconstruction_loss: 13102.1211 - vqvae_loss: 758552.3786\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771693.1142 - reconstruction_loss: 13102.1211 - vqvae_loss: 758552.3786\n",
            "Epoch 2280/2500\n",
            "Epoch 2280/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 772414.1815 - reconstruction_loss: 13049.7686 - vqvae_loss: 759323.9808\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 772414.1815 - reconstruction_loss: 13049.7686 - vqvae_loss: 759323.9808\n",
            "Epoch 2281/2500\n",
            "Epoch 2281/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776234.8726 - reconstruction_loss: 13061.2490 - vqvae_loss: 763047.0853\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776234.8726 - reconstruction_loss: 13061.2490 - vqvae_loss: 763047.0853\n",
            "Epoch 2282/2500\n",
            "Epoch 2282/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 776350.4459 - reconstruction_loss: 13076.8555 - vqvae_loss: 763309.7740\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 776350.4459 - reconstruction_loss: 13076.8555 - vqvae_loss: 763309.7740\n",
            "Epoch 2283/2500\n",
            "Epoch 2283/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 774871.5745 - reconstruction_loss: 13087.1875 - vqvae_loss: 761784.8558\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 774871.5745 - reconstruction_loss: 13087.1875 - vqvae_loss: 761784.8558\n",
            "Epoch 2284/2500\n",
            "Epoch 2284/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 785289.6142 - reconstruction_loss: 13084.8799 - vqvae_loss: 772187.7524\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 785289.6142 - reconstruction_loss: 13084.8799 - vqvae_loss: 772187.7524\n",
            "Epoch 2285/2500\n",
            "Epoch 2285/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 777730.9399 - reconstruction_loss: 13057.4170 - vqvae_loss: 764720.4399\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 777730.9399 - reconstruction_loss: 13057.4170 - vqvae_loss: 764720.4399\n",
            "Epoch 2286/2500\n",
            "Epoch 2286/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780925.7296 - reconstruction_loss: 13039.7891 - vqvae_loss: 767975.1779\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780925.7296 - reconstruction_loss: 13039.7891 - vqvae_loss: 767975.1779\n",
            "Epoch 2287/2500\n",
            "Epoch 2287/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 784824.1995 - reconstruction_loss: 13038.2549 - vqvae_loss: 771734.9615\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 784824.1995 - reconstruction_loss: 13038.2549 - vqvae_loss: 771734.9615\n",
            "Epoch 2288/2500\n",
            "Epoch 2288/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 780945.7837 - reconstruction_loss: 13061.1719 - vqvae_loss: 767909.9483\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 780945.7837 - reconstruction_loss: 13061.1719 - vqvae_loss: 767909.9483\n",
            "Epoch 2289/2500\n",
            "Epoch 2289/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 782334.1827 - reconstruction_loss: 13049.0771 - vqvae_loss: 769363.5024\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 782334.1827 - reconstruction_loss: 13049.0771 - vqvae_loss: 769363.5024\n",
            "Epoch 2290/2500\n",
            "Epoch 2290/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 787363.9952 - reconstruction_loss: 13043.2529 - vqvae_loss: 774335.4627\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 787363.9952 - reconstruction_loss: 13043.2529 - vqvae_loss: 774335.4627\n",
            "Epoch 2291/2500\n",
            "Epoch 2291/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788572.5024 - reconstruction_loss: 13122.9141 - vqvae_loss: 775494.8077\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788572.5024 - reconstruction_loss: 13122.9141 - vqvae_loss: 775494.8077\n",
            "Epoch 2292/2500\n",
            "Epoch 2292/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784760.5625 - reconstruction_loss: 13122.3994 - vqvae_loss: 771616.0288\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784760.5625 - reconstruction_loss: 13122.3994 - vqvae_loss: 771616.0288\n",
            "Epoch 2293/2500\n",
            "Epoch 2293/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 782582.8293 - reconstruction_loss: 13085.0332 - vqvae_loss: 769485.7921\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 782582.8293 - reconstruction_loss: 13085.0332 - vqvae_loss: 769485.7921\n",
            "Epoch 2294/2500\n",
            "Epoch 2294/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 781174.2656 - reconstruction_loss: 13125.9678 - vqvae_loss: 768092.4555\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 781174.2656 - reconstruction_loss: 13125.9678 - vqvae_loss: 768092.4555\n",
            "Epoch 2295/2500\n",
            "Epoch 2295/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788331.5757 - reconstruction_loss: 13148.3398 - vqvae_loss: 775165.7608\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788331.5757 - reconstruction_loss: 13148.3398 - vqvae_loss: 775165.7608\n",
            "Epoch 2296/2500\n",
            "Epoch 2296/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 789145.0108 - reconstruction_loss: 13142.3301 - vqvae_loss: 776039.6719\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 789145.0108 - reconstruction_loss: 13142.3301 - vqvae_loss: 776039.6719\n",
            "Epoch 2297/2500\n",
            "Epoch 2297/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784316.9760 - reconstruction_loss: 13133.3711 - vqvae_loss: 771188.2079\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784316.9760 - reconstruction_loss: 13133.3711 - vqvae_loss: 771188.2079\n",
            "Epoch 2298/2500\n",
            "Epoch 2298/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 783730.8053 - reconstruction_loss: 13106.4629 - vqvae_loss: 770616.1142\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 783730.8053 - reconstruction_loss: 13106.4629 - vqvae_loss: 770616.1142\n",
            "Epoch 2299/2500\n",
            "Epoch 2299/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775716.0865 - reconstruction_loss: 13096.5283 - vqvae_loss: 762619.7656\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775716.0865 - reconstruction_loss: 13096.5283 - vqvae_loss: 762619.7656\n",
            "Epoch 2300/2500\n",
            "Epoch 2300/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780851.5517 - reconstruction_loss: 13127.2021 - vqvae_loss: 767725.4760\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 780851.5517 - reconstruction_loss: 13127.2021 - vqvae_loss: 767725.4760\n",
            "Epoch 2301/2500\n",
            "Epoch 2301/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 779745.2332 - reconstruction_loss: 13125.6865 - vqvae_loss: 766605.8786\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 779745.2332 - reconstruction_loss: 13125.6865 - vqvae_loss: 766605.8786\n",
            "Epoch 2302/2500\n",
            "Epoch 2302/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 773209.4976 - reconstruction_loss: 13078.5596 - vqvae_loss: 760176.1827\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 773209.4976 - reconstruction_loss: 13078.5596 - vqvae_loss: 760176.1827\n",
            "Epoch 2303/2500\n",
            "Epoch 2303/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773747.0361 - reconstruction_loss: 13134.0420 - vqvae_loss: 760635.2115\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773747.0361 - reconstruction_loss: 13134.0420 - vqvae_loss: 760635.2115\n",
            "Epoch 2304/2500\n",
            "Epoch 2304/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773494.6478 - reconstruction_loss: 13062.9854 - vqvae_loss: 760432.2007\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773494.6478 - reconstruction_loss: 13062.9854 - vqvae_loss: 760432.2007\n",
            "Epoch 2305/2500\n",
            "Epoch 2305/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 769168.3113 - reconstruction_loss: 13139.2744 - vqvae_loss: 756086.4147\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 769168.3113 - reconstruction_loss: 13139.2744 - vqvae_loss: 756086.4147\n",
            "Epoch 2306/2500\n",
            "Epoch 2306/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 778923.8413 - reconstruction_loss: 13107.4219 - vqvae_loss: 765782.7788\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 778923.8413 - reconstruction_loss: 13107.4219 - vqvae_loss: 765782.7788\n",
            "Epoch 2307/2500\n",
            "Epoch 2307/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772255.5697 - reconstruction_loss: 13113.6387 - vqvae_loss: 759185.5036\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 772255.5697 - reconstruction_loss: 13113.6387 - vqvae_loss: 759185.5036\n",
            "Epoch 2308/2500\n",
            "Epoch 2308/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 779208.4832 - reconstruction_loss: 13092.6924 - vqvae_loss: 766040.4483\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 779208.4832 - reconstruction_loss: 13092.6924 - vqvae_loss: 766040.4483\n",
            "Epoch 2309/2500\n",
            "Epoch 2309/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 770321.2957 - reconstruction_loss: 13130.7773 - vqvae_loss: 757219.7476\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 770321.2957 - reconstruction_loss: 13130.7773 - vqvae_loss: 757219.7476\n",
            "Epoch 2310/2500\n",
            "Epoch 2310/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775660.0697 - reconstruction_loss: 13095.4678 - vqvae_loss: 762551.1346\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775660.0697 - reconstruction_loss: 13095.4678 - vqvae_loss: 762551.1346\n",
            "Epoch 2311/2500\n",
            "Epoch 2311/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 774627.7608 - reconstruction_loss: 13085.5420 - vqvae_loss: 761500.6995\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 774627.7608 - reconstruction_loss: 13085.5420 - vqvae_loss: 761500.6995\n",
            "Epoch 2312/2500\n",
            "Epoch 2312/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 771915.4267 - reconstruction_loss: 13074.3428 - vqvae_loss: 758848.5216\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 771915.4267 - reconstruction_loss: 13074.3428 - vqvae_loss: 758848.5216\n",
            "Epoch 2313/2500\n",
            "Epoch 2313/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 769617.7512 - reconstruction_loss: 13064.2002 - vqvae_loss: 756513.9483\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 769617.7512 - reconstruction_loss: 13064.2002 - vqvae_loss: 756513.9483\n",
            "Epoch 2314/2500\n",
            "Epoch 2314/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765065.3606 - reconstruction_loss: 13115.2783 - vqvae_loss: 752031.5168\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765065.3606 - reconstruction_loss: 13115.2783 - vqvae_loss: 752031.5168\n",
            "Epoch 2315/2500\n",
            "Epoch 2315/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 767554.3221 - reconstruction_loss: 13111.5146 - vqvae_loss: 754476.6947\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 767554.3221 - reconstruction_loss: 13111.5146 - vqvae_loss: 754476.6947\n",
            "Epoch 2316/2500\n",
            "Epoch 2316/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 761362.4663 - reconstruction_loss: 13090.1494 - vqvae_loss: 748361.8570\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 761362.4663 - reconstruction_loss: 13090.1494 - vqvae_loss: 748361.8570\n",
            "Epoch 2317/2500\n",
            "Epoch 2317/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768920.7536 - reconstruction_loss: 13088.9199 - vqvae_loss: 755801.2464\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768920.7536 - reconstruction_loss: 13088.9199 - vqvae_loss: 755801.2464\n",
            "Epoch 2318/2500\n",
            "Epoch 2318/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773377.4507 - reconstruction_loss: 13131.3301 - vqvae_loss: 760174.9038\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773377.4507 - reconstruction_loss: 13131.3301 - vqvae_loss: 760174.9038\n",
            "Epoch 2319/2500\n",
            "Epoch 2319/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 762882.0877 - reconstruction_loss: 13178.6445 - vqvae_loss: 749792.9447\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 762882.0877 - reconstruction_loss: 13178.6445 - vqvae_loss: 749792.9447\n",
            "Epoch 2320/2500\n",
            "Epoch 2320/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 771718.3041 - reconstruction_loss: 13157.9189 - vqvae_loss: 758508.0421\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 771718.3041 - reconstruction_loss: 13157.9189 - vqvae_loss: 758508.0421\n",
            "Epoch 2321/2500\n",
            "Epoch 2321/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770314.5613 - reconstruction_loss: 13149.4326 - vqvae_loss: 757213.3089\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770314.5613 - reconstruction_loss: 13149.4326 - vqvae_loss: 757213.3089\n",
            "Epoch 2322/2500\n",
            "Epoch 2322/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 774323.8365 - reconstruction_loss: 13185.9277 - vqvae_loss: 761149.5781\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 774323.8365 - reconstruction_loss: 13185.9277 - vqvae_loss: 761149.5781\n",
            "Epoch 2323/2500\n",
            "Epoch 2323/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 774770.7764 - reconstruction_loss: 13240.3047 - vqvae_loss: 761618.8389\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 774770.7764 - reconstruction_loss: 13240.3047 - vqvae_loss: 761618.8389\n",
            "Epoch 2324/2500\n",
            "Epoch 2324/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770559.0505 - reconstruction_loss: 13223.8447 - vqvae_loss: 757409.7139\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770559.0505 - reconstruction_loss: 13223.8447 - vqvae_loss: 757409.7139\n",
            "Epoch 2325/2500\n",
            "Epoch 2325/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 768504.3221 - reconstruction_loss: 13123.3545 - vqvae_loss: 755381.5409\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 768504.3221 - reconstruction_loss: 13123.3545 - vqvae_loss: 755381.5409\n",
            "Epoch 2326/2500\n",
            "Epoch 2326/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 769490.9279 - reconstruction_loss: 13168.2451 - vqvae_loss: 756264.1286\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 769490.9279 - reconstruction_loss: 13168.2451 - vqvae_loss: 756264.1286\n",
            "Epoch 2327/2500\n",
            "Epoch 2327/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765078.0180 - reconstruction_loss: 13153.6875 - vqvae_loss: 751957.5325\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765078.0180 - reconstruction_loss: 13153.6875 - vqvae_loss: 751957.5325\n",
            "Epoch 2328/2500\n",
            "Epoch 2328/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 767252.0553 - reconstruction_loss: 13181.6631 - vqvae_loss: 754055.2776\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 767252.0553 - reconstruction_loss: 13181.6631 - vqvae_loss: 754055.2776\n",
            "Epoch 2329/2500\n",
            "Epoch 2329/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 769684.1538 - reconstruction_loss: 13162.6631 - vqvae_loss: 756616.0060\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 769684.1538 - reconstruction_loss: 13162.6631 - vqvae_loss: 756616.0060\n",
            "Epoch 2330/2500\n",
            "Epoch 2330/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771577.3269 - reconstruction_loss: 13197.3027 - vqvae_loss: 758452.0673\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771577.3269 - reconstruction_loss: 13197.3027 - vqvae_loss: 758452.0673\n",
            "Epoch 2331/2500\n",
            "Epoch 2331/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 772585.5060 - reconstruction_loss: 13263.3652 - vqvae_loss: 759302.9976\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 772585.5060 - reconstruction_loss: 13263.3652 - vqvae_loss: 759302.9976\n",
            "Epoch 2332/2500\n",
            "Epoch 2332/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770125.4291 - reconstruction_loss: 13166.8779 - vqvae_loss: 756945.5601\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 770125.4291 - reconstruction_loss: 13166.8779 - vqvae_loss: 756945.5601\n",
            "Epoch 2333/2500\n",
            "Epoch 2333/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 777390.9904 - reconstruction_loss: 13191.4482 - vqvae_loss: 764171.8594\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 777390.9904 - reconstruction_loss: 13191.4482 - vqvae_loss: 764171.8594\n",
            "Epoch 2334/2500\n",
            "Epoch 2334/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775782.9952 - reconstruction_loss: 13219.3623 - vqvae_loss: 762542.5096\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 775782.9952 - reconstruction_loss: 13219.3623 - vqvae_loss: 762542.5096\n",
            "Epoch 2335/2500\n",
            "Epoch 2335/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776905.4952 - reconstruction_loss: 13225.6885 - vqvae_loss: 763658.0493\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 776905.4952 - reconstruction_loss: 13225.6885 - vqvae_loss: 763658.0493\n",
            "Epoch 2336/2500\n",
            "Epoch 2336/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768904.2512 - reconstruction_loss: 13197.1982 - vqvae_loss: 755846.4411\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768904.2512 - reconstruction_loss: 13197.1982 - vqvae_loss: 755846.4411\n",
            "Epoch 2337/2500\n",
            "Epoch 2337/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771212.8906 - reconstruction_loss: 13195.9121 - vqvae_loss: 758002.8810\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771212.8906 - reconstruction_loss: 13195.9121 - vqvae_loss: 758002.8810\n",
            "Epoch 2338/2500\n",
            "Epoch 2338/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 768877.2560 - reconstruction_loss: 13190.4756 - vqvae_loss: 755655.4399\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 768877.2560 - reconstruction_loss: 13190.4756 - vqvae_loss: 755655.4399\n",
            "Epoch 2339/2500\n",
            "Epoch 2339/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 765738.7127 - reconstruction_loss: 13263.0781 - vqvae_loss: 752550.1226\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 765738.7127 - reconstruction_loss: 13263.0781 - vqvae_loss: 752550.1226\n",
            "Epoch 2340/2500\n",
            "Epoch 2340/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765289.8618 - reconstruction_loss: 13215.9082 - vqvae_loss: 752051.0300\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765289.8618 - reconstruction_loss: 13215.9082 - vqvae_loss: 752051.0300\n",
            "Epoch 2341/2500\n",
            "Epoch 2341/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771483.1094 - reconstruction_loss: 13254.0957 - vqvae_loss: 758139.3401\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771483.1094 - reconstruction_loss: 13254.0957 - vqvae_loss: 758139.3401\n",
            "Epoch 2342/2500\n",
            "Epoch 2342/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 767420.3798 - reconstruction_loss: 13279.0176 - vqvae_loss: 754118.7344\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 767420.3798 - reconstruction_loss: 13279.0176 - vqvae_loss: 754118.7344\n",
            "Epoch 2343/2500\n",
            "Epoch 2343/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768043.9026 - reconstruction_loss: 13232.5088 - vqvae_loss: 754774.7224\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 768043.9026 - reconstruction_loss: 13232.5088 - vqvae_loss: 754774.7224\n",
            "Epoch 2344/2500\n",
            "Epoch 2344/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 779331.4555 - reconstruction_loss: 13268.1152 - vqvae_loss: 765999.2572\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 779331.4555 - reconstruction_loss: 13268.1152 - vqvae_loss: 765999.2572\n",
            "Epoch 2345/2500\n",
            "Epoch 2345/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771986.5841 - reconstruction_loss: 13248.0928 - vqvae_loss: 758745.7608\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 771986.5841 - reconstruction_loss: 13248.0928 - vqvae_loss: 758745.7608\n",
            "Epoch 2346/2500\n",
            "Epoch 2346/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771503.4627 - reconstruction_loss: 13254.1914 - vqvae_loss: 758242.3774\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771503.4627 - reconstruction_loss: 13254.1914 - vqvae_loss: 758242.3774\n",
            "Epoch 2347/2500\n",
            "Epoch 2347/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 776854.3101 - reconstruction_loss: 13293.7373 - vqvae_loss: 763537.0312\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 776854.3101 - reconstruction_loss: 13293.7373 - vqvae_loss: 763537.0312\n",
            "Epoch 2348/2500\n",
            "Epoch 2348/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773649.9195 - reconstruction_loss: 13291.7578 - vqvae_loss: 760359.8233\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773649.9195 - reconstruction_loss: 13291.7578 - vqvae_loss: 760359.8233\n",
            "Epoch 2349/2500\n",
            "Epoch 2349/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 782422.0913 - reconstruction_loss: 13300.1162 - vqvae_loss: 769085.9916\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 782422.0913 - reconstruction_loss: 13300.1162 - vqvae_loss: 769085.9916\n",
            "Epoch 2350/2500\n",
            "Epoch 2350/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 782994.1346 - reconstruction_loss: 13325.2373 - vqvae_loss: 769737.7981\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 782994.1346 - reconstruction_loss: 13325.2373 - vqvae_loss: 769737.7981\n",
            "Epoch 2351/2500\n",
            "Epoch 2351/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 780988.5877 - reconstruction_loss: 13298.4121 - vqvae_loss: 767659.0877\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 780988.5877 - reconstruction_loss: 13298.4121 - vqvae_loss: 767659.0877\n",
            "Epoch 2352/2500\n",
            "Epoch 2352/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 787153.3738 - reconstruction_loss: 13294.8428 - vqvae_loss: 773894.4171\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 787153.3738 - reconstruction_loss: 13294.8428 - vqvae_loss: 773894.4171\n",
            "Epoch 2353/2500\n",
            "Epoch 2353/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 787068.4603 - reconstruction_loss: 13309.9170 - vqvae_loss: 773702.3077\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 787068.4603 - reconstruction_loss: 13309.9170 - vqvae_loss: 773702.3077\n",
            "Epoch 2354/2500\n",
            "Epoch 2354/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 786232.8401 - reconstruction_loss: 13269.7686 - vqvae_loss: 772976.1514\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 786232.8401 - reconstruction_loss: 13269.7686 - vqvae_loss: 772976.1514\n",
            "Epoch 2355/2500\n",
            "Epoch 2355/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784324.2163 - reconstruction_loss: 13363.3213 - vqvae_loss: 771087.0433\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 784324.2163 - reconstruction_loss: 13363.3213 - vqvae_loss: 771087.0433\n",
            "Epoch 2356/2500\n",
            "Epoch 2356/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 793165.0541 - reconstruction_loss: 13372.9277 - vqvae_loss: 779807.2007\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 793165.0541 - reconstruction_loss: 13372.9277 - vqvae_loss: 779807.2007\n",
            "Epoch 2357/2500\n",
            "Epoch 2357/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 790656.5397 - reconstruction_loss: 13289.1074 - vqvae_loss: 777393.2837\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 790656.5397 - reconstruction_loss: 13289.1074 - vqvae_loss: 777393.2837\n",
            "Epoch 2358/2500\n",
            "Epoch 2358/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 783093.4255 - reconstruction_loss: 13277.6953 - vqvae_loss: 769889.7788\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 783093.4255 - reconstruction_loss: 13277.6953 - vqvae_loss: 769889.7788\n",
            "Epoch 2359/2500\n",
            "Epoch 2359/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 786523.8425 - reconstruction_loss: 13296.5654 - vqvae_loss: 773192.1731\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 786523.8425 - reconstruction_loss: 13296.5654 - vqvae_loss: 773192.1731\n",
            "Epoch 2360/2500\n",
            "Epoch 2360/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 783430.3474 - reconstruction_loss: 13306.5996 - vqvae_loss: 770089.3858\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 783430.3474 - reconstruction_loss: 13306.5996 - vqvae_loss: 770089.3858\n",
            "Epoch 2361/2500\n",
            "Epoch 2361/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 773968.9062 - reconstruction_loss: 13258.0254 - vqvae_loss: 760761.1599\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 773968.9062 - reconstruction_loss: 13258.0254 - vqvae_loss: 760761.1599\n",
            "Epoch 2362/2500\n",
            "Epoch 2362/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788976.2704 - reconstruction_loss: 13275.5977 - vqvae_loss: 775543.5613\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 788976.2704 - reconstruction_loss: 13275.5977 - vqvae_loss: 775543.5613\n",
            "Epoch 2363/2500\n",
            "Epoch 2363/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 779702.4423 - reconstruction_loss: 13221.5176 - vqvae_loss: 766569.1827\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 779702.4423 - reconstruction_loss: 13221.5176 - vqvae_loss: 766569.1827\n",
            "Epoch 2364/2500\n",
            "Epoch 2364/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 781490.3966 - reconstruction_loss: 13243.1963 - vqvae_loss: 768364.3161\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 781490.3966 - reconstruction_loss: 13243.1963 - vqvae_loss: 768364.3161\n",
            "Epoch 2365/2500\n",
            "Epoch 2365/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773149.6899 - reconstruction_loss: 13201.0254 - vqvae_loss: 759970.5625\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 773149.6899 - reconstruction_loss: 13201.0254 - vqvae_loss: 759970.5625\n",
            "Epoch 2366/2500\n",
            "Epoch 2366/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 778192.1743 - reconstruction_loss: 13235.7900 - vqvae_loss: 764888.9760\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 778192.1743 - reconstruction_loss: 13235.7900 - vqvae_loss: 764888.9760\n",
            "Epoch 2367/2500\n",
            "Epoch 2367/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771762.0721 - reconstruction_loss: 13219.2354 - vqvae_loss: 758442.7608\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 771762.0721 - reconstruction_loss: 13219.2354 - vqvae_loss: 758442.7608\n",
            "Epoch 2368/2500\n",
            "Epoch 2368/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 767053.2404 - reconstruction_loss: 13152.6865 - vqvae_loss: 753933.1887\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 767053.2404 - reconstruction_loss: 13152.6865 - vqvae_loss: 753933.1887\n",
            "Epoch 2369/2500\n",
            "Epoch 2369/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 769401.3702 - reconstruction_loss: 13182.5176 - vqvae_loss: 756245.8233\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 769401.3702 - reconstruction_loss: 13182.5176 - vqvae_loss: 756245.8233\n",
            "Epoch 2370/2500\n",
            "Epoch 2370/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 766217.8425 - reconstruction_loss: 13192.3672 - vqvae_loss: 753059.0841\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 766217.8425 - reconstruction_loss: 13192.3672 - vqvae_loss: 753059.0841\n",
            "Epoch 2371/2500\n",
            "Epoch 2371/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765241.2572 - reconstruction_loss: 13245.6455 - vqvae_loss: 751945.3666\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 765241.2572 - reconstruction_loss: 13245.6455 - vqvae_loss: 751945.3666\n",
            "Epoch 2372/2500\n",
            "Epoch 2372/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 764073.2067 - reconstruction_loss: 13238.0342 - vqvae_loss: 750800.7897\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 764073.2067 - reconstruction_loss: 13238.0342 - vqvae_loss: 750800.7897\n",
            "Epoch 2373/2500\n",
            "Epoch 2373/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 757919.6478 - reconstruction_loss: 13248.5234 - vqvae_loss: 744741.0505\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 757919.6478 - reconstruction_loss: 13248.5234 - vqvae_loss: 744741.0505\n",
            "Epoch 2374/2500\n",
            "Epoch 2374/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 764161.8690 - reconstruction_loss: 13270.6201 - vqvae_loss: 750844.5757\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 764161.8690 - reconstruction_loss: 13270.6201 - vqvae_loss: 750844.5757\n",
            "Epoch 2375/2500\n",
            "Epoch 2375/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 756807.6106 - reconstruction_loss: 13295.1250 - vqvae_loss: 743514.2272\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 756807.6106 - reconstruction_loss: 13295.1250 - vqvae_loss: 743514.2272\n",
            "Epoch 2376/2500\n",
            "Epoch 2376/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 754755.2548 - reconstruction_loss: 13267.4424 - vqvae_loss: 741401.5264\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 754755.2548 - reconstruction_loss: 13267.4424 - vqvae_loss: 741401.5264\n",
            "Epoch 2377/2500\n",
            "Epoch 2377/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753820.6334 - reconstruction_loss: 13293.7695 - vqvae_loss: 740544.5938\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 753820.6334 - reconstruction_loss: 13293.7695 - vqvae_loss: 740544.5938\n",
            "Epoch 2378/2500\n",
            "Epoch 2378/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 747168.1442 - reconstruction_loss: 13274.9668 - vqvae_loss: 733971.4808\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 747168.1442 - reconstruction_loss: 13274.9668 - vqvae_loss: 733971.4808\n",
            "Epoch 2379/2500\n",
            "Epoch 2379/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 743408.4339 - reconstruction_loss: 13361.8369 - vqvae_loss: 730038.6647\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 743408.4339 - reconstruction_loss: 13361.8369 - vqvae_loss: 730038.6647\n",
            "Epoch 2380/2500\n",
            "Epoch 2380/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 743475.9964 - reconstruction_loss: 13337.8623 - vqvae_loss: 730087.1707\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 743475.9964 - reconstruction_loss: 13337.8623 - vqvae_loss: 730087.1707\n",
            "Epoch 2381/2500\n",
            "Epoch 2381/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 743488.0974 - reconstruction_loss: 13414.5127 - vqvae_loss: 729987.2248\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 743488.0974 - reconstruction_loss: 13414.5127 - vqvae_loss: 729987.2248\n",
            "Epoch 2382/2500\n",
            "Epoch 2382/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 735206.5950 - reconstruction_loss: 13323.6904 - vqvae_loss: 721931.3293\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 735206.5950 - reconstruction_loss: 13323.6904 - vqvae_loss: 721931.3293\n",
            "Epoch 2383/2500\n",
            "Epoch 2383/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 734474.6250 - reconstruction_loss: 13296.7744 - vqvae_loss: 721210.5216\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 734474.6250 - reconstruction_loss: 13296.7744 - vqvae_loss: 721210.5216\n",
            "Epoch 2384/2500\n",
            "Epoch 2384/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 734467.4255 - reconstruction_loss: 13323.6143 - vqvae_loss: 721176.4952\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 734467.4255 - reconstruction_loss: 13323.6143 - vqvae_loss: 721176.4952\n",
            "Epoch 2385/2500\n",
            "Epoch 2385/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 736821.4615 - reconstruction_loss: 13319.1533 - vqvae_loss: 723425.7752\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 736821.4615 - reconstruction_loss: 13319.1533 - vqvae_loss: 723425.7752\n",
            "Epoch 2386/2500\n",
            "Epoch 2386/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 732874.0709 - reconstruction_loss: 13312.0127 - vqvae_loss: 719587.7007\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 732874.0709 - reconstruction_loss: 13312.0127 - vqvae_loss: 719587.7007\n",
            "Epoch 2387/2500\n",
            "Epoch 2387/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 735328.0974 - reconstruction_loss: 13310.2744 - vqvae_loss: 721930.5685\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 735328.0974 - reconstruction_loss: 13310.2744 - vqvae_loss: 721930.5685\n",
            "Epoch 2388/2500\n",
            "Epoch 2388/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 723681.8726 - reconstruction_loss: 13335.3369 - vqvae_loss: 710409.9784\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 723681.8726 - reconstruction_loss: 13335.3369 - vqvae_loss: 710409.9784\n",
            "Epoch 2389/2500\n",
            "Epoch 2389/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 730169.1274 - reconstruction_loss: 13338.5283 - vqvae_loss: 716827.4483\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 730169.1274 - reconstruction_loss: 13338.5283 - vqvae_loss: 716827.4483\n",
            "Epoch 2390/2500\n",
            "Epoch 2390/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 723722.3978 - reconstruction_loss: 13306.6592 - vqvae_loss: 710551.9014\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 723722.3978 - reconstruction_loss: 13306.6592 - vqvae_loss: 710551.9014\n",
            "Epoch 2391/2500\n",
            "Epoch 2391/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 720869.5998 - reconstruction_loss: 13299.9756 - vqvae_loss: 707463.4724\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 720869.5998 - reconstruction_loss: 13299.9756 - vqvae_loss: 707463.4724\n",
            "Epoch 2392/2500\n",
            "Epoch 2392/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 719679.7404 - reconstruction_loss: 13229.7051 - vqvae_loss: 706323.4603\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 719679.7404 - reconstruction_loss: 13229.7051 - vqvae_loss: 706323.4603\n",
            "Epoch 2393/2500\n",
            "Epoch 2393/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 721149.1791 - reconstruction_loss: 13243.6377 - vqvae_loss: 707873.3966\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 721149.1791 - reconstruction_loss: 13243.6377 - vqvae_loss: 707873.3966\n",
            "Epoch 2394/2500\n",
            "Epoch 2394/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 712877.6839 - reconstruction_loss: 13226.6279 - vqvae_loss: 699634.6623\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 712877.6839 - reconstruction_loss: 13226.6279 - vqvae_loss: 699634.6623\n",
            "Epoch 2395/2500\n",
            "Epoch 2395/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 708689.2572 - reconstruction_loss: 13224.5195 - vqvae_loss: 695458.9219\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 708689.2572 - reconstruction_loss: 13224.5195 - vqvae_loss: 695458.9219\n",
            "Epoch 2396/2500\n",
            "Epoch 2396/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 707785.9435 - reconstruction_loss: 13207.4453 - vqvae_loss: 694480.7055\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 707785.9435 - reconstruction_loss: 13207.4453 - vqvae_loss: 694480.7055\n",
            "Epoch 2397/2500\n",
            "Epoch 2397/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 697593.5180 - reconstruction_loss: 13183.3301 - vqvae_loss: 684400.8522\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 697593.5180 - reconstruction_loss: 13183.3301 - vqvae_loss: 684400.8522\n",
            "Epoch 2398/2500\n",
            "Epoch 2398/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 698196.9123 - reconstruction_loss: 13233.3496 - vqvae_loss: 684862.2163\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 698196.9123 - reconstruction_loss: 13233.3496 - vqvae_loss: 684862.2163\n",
            "Epoch 2399/2500\n",
            "Epoch 2399/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 696949.3077 - reconstruction_loss: 13231.8408 - vqvae_loss: 683564.4988\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 696949.3077 - reconstruction_loss: 13231.8408 - vqvae_loss: 683564.4988\n",
            "Epoch 2400/2500\n",
            "Epoch 2400/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 687881.1166 - reconstruction_loss: 13234.2080 - vqvae_loss: 674670.6526\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 687881.1166 - reconstruction_loss: 13234.2080 - vqvae_loss: 674670.6526\n",
            "Epoch 2401/2500\n",
            "Epoch 2401/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 692576.8990 - reconstruction_loss: 13244.4219 - vqvae_loss: 679359.3786\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 692576.8990 - reconstruction_loss: 13244.4219 - vqvae_loss: 679359.3786\n",
            "Epoch 2402/2500\n",
            "Epoch 2402/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 693567.1599 - reconstruction_loss: 13250.1406 - vqvae_loss: 680262.3858\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 693567.1599 - reconstruction_loss: 13250.1406 - vqvae_loss: 680262.3858\n",
            "Epoch 2403/2500\n",
            "Epoch 2403/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 685517.8846 - reconstruction_loss: 13249.2061 - vqvae_loss: 672335.9976\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 685517.8846 - reconstruction_loss: 13249.2061 - vqvae_loss: 672335.9976\n",
            "Epoch 2404/2500\n",
            "Epoch 2404/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 689632.2031 - reconstruction_loss: 13277.0967 - vqvae_loss: 676317.7380\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 689632.2031 - reconstruction_loss: 13277.0967 - vqvae_loss: 676317.7380\n",
            "Epoch 2405/2500\n",
            "Epoch 2405/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 689123.5132 - reconstruction_loss: 13237.5322 - vqvae_loss: 675836.2428\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 689123.5132 - reconstruction_loss: 13237.5322 - vqvae_loss: 675836.2428\n",
            "Epoch 2406/2500\n",
            "Epoch 2406/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 682635.9820 - reconstruction_loss: 13239.5801 - vqvae_loss: 669349.5817\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 682635.9820 - reconstruction_loss: 13239.5801 - vqvae_loss: 669349.5817\n",
            "Epoch 2407/2500\n",
            "Epoch 2407/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 686283.5877 - reconstruction_loss: 13248.2061 - vqvae_loss: 672991.3377\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 686283.5877 - reconstruction_loss: 13248.2061 - vqvae_loss: 672991.3377\n",
            "Epoch 2408/2500\n",
            "Epoch 2408/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 687117.6719 - reconstruction_loss: 13256.3809 - vqvae_loss: 673853.5469\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 687117.6719 - reconstruction_loss: 13256.3809 - vqvae_loss: 673853.5469\n",
            "Epoch 2409/2500\n",
            "Epoch 2409/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 691119.4856 - reconstruction_loss: 13220.2012 - vqvae_loss: 677776.8774\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 691119.4856 - reconstruction_loss: 13220.2012 - vqvae_loss: 677776.8774\n",
            "Epoch 2410/2500\n",
            "Epoch 2410/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 685736.0757 - reconstruction_loss: 13254.7627 - vqvae_loss: 672487.3365\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 685736.0757 - reconstruction_loss: 13254.7627 - vqvae_loss: 672487.3365\n",
            "Epoch 2411/2500\n",
            "Epoch 2411/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 679263.4375 - reconstruction_loss: 13297.8301 - vqvae_loss: 665891.9483\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 679263.4375 - reconstruction_loss: 13297.8301 - vqvae_loss: 665891.9483\n",
            "Epoch 2412/2500\n",
            "Epoch 2412/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 681817.1082 - reconstruction_loss: 13292.7832 - vqvae_loss: 668560.0192\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 681817.1082 - reconstruction_loss: 13292.7832 - vqvae_loss: 668560.0192\n",
            "Epoch 2413/2500\n",
            "Epoch 2413/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 677052.4784 - reconstruction_loss: 13241.4561 - vqvae_loss: 663828.6298\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 677052.4784 - reconstruction_loss: 13241.4561 - vqvae_loss: 663828.6298\n",
            "Epoch 2414/2500\n",
            "Epoch 2414/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 674493.0625 - reconstruction_loss: 13280.7549 - vqvae_loss: 661308.1358\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 674493.0625 - reconstruction_loss: 13280.7549 - vqvae_loss: 661308.1358\n",
            "Epoch 2415/2500\n",
            "Epoch 2415/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 680628.8293 - reconstruction_loss: 13265.2500 - vqvae_loss: 667391.7560\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 680628.8293 - reconstruction_loss: 13265.2500 - vqvae_loss: 667391.7560\n",
            "Epoch 2416/2500\n",
            "Epoch 2416/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 672548.9411 - reconstruction_loss: 13270.8652 - vqvae_loss: 659376.7067\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 672548.9411 - reconstruction_loss: 13270.8652 - vqvae_loss: 659376.7067\n",
            "Epoch 2417/2500\n",
            "Epoch 2417/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 675393.9796 - reconstruction_loss: 13256.1553 - vqvae_loss: 662130.6671\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 675393.9796 - reconstruction_loss: 13256.1553 - vqvae_loss: 662130.6671\n",
            "Epoch 2418/2500\n",
            "Epoch 2418/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 674911.2897 - reconstruction_loss: 13271.2178 - vqvae_loss: 661548.4423\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 674911.2897 - reconstruction_loss: 13271.2178 - vqvae_loss: 661548.4423\n",
            "Epoch 2419/2500\n",
            "Epoch 2419/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 670032.1370 - reconstruction_loss: 13240.1143 - vqvae_loss: 656717.3137\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 670032.1370 - reconstruction_loss: 13240.1143 - vqvae_loss: 656717.3137\n",
            "Epoch 2420/2500\n",
            "Epoch 2420/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 669926.1839 - reconstruction_loss: 13236.8936 - vqvae_loss: 656682.4050\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 669926.1839 - reconstruction_loss: 13236.8936 - vqvae_loss: 656682.4050\n",
            "Epoch 2421/2500\n",
            "Epoch 2421/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 667205.3642 - reconstruction_loss: 13235.9463 - vqvae_loss: 653978.8750\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 667205.3642 - reconstruction_loss: 13235.9463 - vqvae_loss: 653978.8750\n",
            "Epoch 2422/2500\n",
            "Epoch 2422/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 663450.2524 - reconstruction_loss: 13233.5439 - vqvae_loss: 650206.2764\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 663450.2524 - reconstruction_loss: 13233.5439 - vqvae_loss: 650206.2764\n",
            "Epoch 2423/2500\n",
            "Epoch 2423/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 667001.0312 - reconstruction_loss: 13246.0244 - vqvae_loss: 653817.3666\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 667001.0312 - reconstruction_loss: 13246.0244 - vqvae_loss: 653817.3666\n",
            "Epoch 2424/2500\n",
            "Epoch 2424/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 671038.1442 - reconstruction_loss: 13206.0137 - vqvae_loss: 657847.5529\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 671038.1442 - reconstruction_loss: 13206.0137 - vqvae_loss: 657847.5529\n",
            "Epoch 2425/2500\n",
            "Epoch 2425/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 673092.0962 - reconstruction_loss: 13178.3076 - vqvae_loss: 659770.8774\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 673092.0962 - reconstruction_loss: 13178.3076 - vqvae_loss: 659770.8774\n",
            "Epoch 2426/2500\n",
            "Epoch 2426/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 662915.6887 - reconstruction_loss: 13256.7744 - vqvae_loss: 649710.3906\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 662915.6887 - reconstruction_loss: 13256.7744 - vqvae_loss: 649710.3906\n",
            "Epoch 2427/2500\n",
            "Epoch 2427/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 664386.5781 - reconstruction_loss: 13193.7051 - vqvae_loss: 651121.6262\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 664386.5781 - reconstruction_loss: 13193.7051 - vqvae_loss: 651121.6262\n",
            "Epoch 2428/2500\n",
            "Epoch 2428/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 656768.6418 - reconstruction_loss: 13168.3408 - vqvae_loss: 643587.5433\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 656768.6418 - reconstruction_loss: 13168.3408 - vqvae_loss: 643587.5433\n",
            "Epoch 2429/2500\n",
            "Epoch 2429/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 658949.9748 - reconstruction_loss: 13202.1729 - vqvae_loss: 645776.7091\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 658949.9748 - reconstruction_loss: 13202.1729 - vqvae_loss: 645776.7091\n",
            "Epoch 2430/2500\n",
            "Epoch 2430/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 658858.5397 - reconstruction_loss: 13135.3252 - vqvae_loss: 645740.7728\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 658858.5397 - reconstruction_loss: 13135.3252 - vqvae_loss: 645740.7728\n",
            "Epoch 2431/2500\n",
            "Epoch 2431/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 659158.6935 - reconstruction_loss: 13150.3838 - vqvae_loss: 645909.0072\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 659158.6935 - reconstruction_loss: 13150.3838 - vqvae_loss: 645909.0072\n",
            "Epoch 2432/2500\n",
            "Epoch 2432/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 660218.8305 - reconstruction_loss: 13184.9434 - vqvae_loss: 647023.3618\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 660218.8305 - reconstruction_loss: 13184.9434 - vqvae_loss: 647023.3618\n",
            "Epoch 2433/2500\n",
            "Epoch 2433/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 654286.4399 - reconstruction_loss: 13115.8359 - vqvae_loss: 641104.8858\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 654286.4399 - reconstruction_loss: 13115.8359 - vqvae_loss: 641104.8858\n",
            "Epoch 2434/2500\n",
            "Epoch 2434/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 646408.4868 - reconstruction_loss: 13064.3721 - vqvae_loss: 633368.7740\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 646408.4868 - reconstruction_loss: 13064.3721 - vqvae_loss: 633368.7740\n",
            "Epoch 2435/2500\n",
            "Epoch 2435/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 643342.4688 - reconstruction_loss: 13089.1523 - vqvae_loss: 630257.3606\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 643342.4688 - reconstruction_loss: 13089.1523 - vqvae_loss: 630257.3606\n",
            "Epoch 2436/2500\n",
            "Epoch 2436/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 648207.1070 - reconstruction_loss: 13078.2070 - vqvae_loss: 635094.3462\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 648207.1070 - reconstruction_loss: 13078.2070 - vqvae_loss: 635094.3462\n",
            "Epoch 2437/2500\n",
            "Epoch 2437/2500\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 646315.3942 - reconstruction_loss: 13098.7510 - vqvae_loss: 633256.7716\n",
            "51/51 [==============================] - 5s 98ms/step - loss: 646315.3942 - reconstruction_loss: 13098.7510 - vqvae_loss: 633256.7716\n",
            "Epoch 2438/2500\n",
            "Epoch 2438/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 643865.9543 - reconstruction_loss: 13089.8457 - vqvae_loss: 630778.0877\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 643865.9543 - reconstruction_loss: 13089.8457 - vqvae_loss: 630778.0877\n",
            "Epoch 2439/2500\n",
            "Epoch 2439/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 643137.6070 - reconstruction_loss: 13071.4297 - vqvae_loss: 630069.6310\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 643137.6070 - reconstruction_loss: 13071.4297 - vqvae_loss: 630069.6310\n",
            "Epoch 2440/2500\n",
            "Epoch 2440/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 650228.1154 - reconstruction_loss: 13077.9678 - vqvae_loss: 637050.3930\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 650228.1154 - reconstruction_loss: 13077.9678 - vqvae_loss: 637050.3930\n",
            "Epoch 2441/2500\n",
            "Epoch 2441/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 643717.1322 - reconstruction_loss: 13027.7627 - vqvae_loss: 630662.7740\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 643717.1322 - reconstruction_loss: 13027.7627 - vqvae_loss: 630662.7740\n",
            "Epoch 2442/2500\n",
            "Epoch 2442/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 640077.6418 - reconstruction_loss: 13146.6445 - vqvae_loss: 626976.0901\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 640077.6418 - reconstruction_loss: 13146.6445 - vqvae_loss: 626976.0901\n",
            "Epoch 2443/2500\n",
            "Epoch 2443/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 649258.4495 - reconstruction_loss: 13534.0957 - vqvae_loss: 635706.9135\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 649258.4495 - reconstruction_loss: 13534.0957 - vqvae_loss: 635706.9135\n",
            "Epoch 2444/2500\n",
            "Epoch 2444/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 639695.4231 - reconstruction_loss: 13276.7275 - vqvae_loss: 626339.0661\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 639695.4231 - reconstruction_loss: 13276.7275 - vqvae_loss: 626339.0661\n",
            "Epoch 2445/2500\n",
            "Epoch 2445/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 638531.4688 - reconstruction_loss: 13220.7275 - vqvae_loss: 625338.6959\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 638531.4688 - reconstruction_loss: 13220.7275 - vqvae_loss: 625338.6959\n",
            "Epoch 2446/2500\n",
            "Epoch 2446/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 631326.8702 - reconstruction_loss: 13243.5283 - vqvae_loss: 617926.5060\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 631326.8702 - reconstruction_loss: 13243.5283 - vqvae_loss: 617926.5060\n",
            "Epoch 2447/2500\n",
            "Epoch 2447/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 617140.5276 - reconstruction_loss: 13234.3926 - vqvae_loss: 603941.4231\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 617140.5276 - reconstruction_loss: 13234.3926 - vqvae_loss: 603941.4231\n",
            "Epoch 2448/2500\n",
            "Epoch 2448/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 616127.9279 - reconstruction_loss: 13251.5879 - vqvae_loss: 602874.5829\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 616127.9279 - reconstruction_loss: 13251.5879 - vqvae_loss: 602874.5829\n",
            "Epoch 2449/2500\n",
            "Epoch 2449/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 614485.6430 - reconstruction_loss: 13178.9619 - vqvae_loss: 601292.3498\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 614485.6430 - reconstruction_loss: 13178.9619 - vqvae_loss: 601292.3498\n",
            "Epoch 2450/2500\n",
            "Epoch 2450/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 617114.9002 - reconstruction_loss: 13157.6221 - vqvae_loss: 603975.3582\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 617114.9002 - reconstruction_loss: 13157.6221 - vqvae_loss: 603975.3582\n",
            "Epoch 2451/2500\n",
            "Epoch 2451/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 595737.0901 - reconstruction_loss: 13111.8047 - vqvae_loss: 582598.4904\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 595737.0901 - reconstruction_loss: 13111.8047 - vqvae_loss: 582598.4904\n",
            "Epoch 2452/2500\n",
            "Epoch 2452/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 593620.2752 - reconstruction_loss: 13112.1309 - vqvae_loss: 580517.6310\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 593620.2752 - reconstruction_loss: 13112.1309 - vqvae_loss: 580517.6310\n",
            "Epoch 2453/2500\n",
            "Epoch 2453/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 592361.1550 - reconstruction_loss: 13074.8887 - vqvae_loss: 579243.7332\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 592361.1550 - reconstruction_loss: 13074.8887 - vqvae_loss: 579243.7332\n",
            "Epoch 2454/2500\n",
            "Epoch 2454/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 583169.0204 - reconstruction_loss: 13077.1602 - vqvae_loss: 570135.6346\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 583169.0204 - reconstruction_loss: 13077.1602 - vqvae_loss: 570135.6346\n",
            "Epoch 2455/2500\n",
            "Epoch 2455/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 581057.8642 - reconstruction_loss: 13049.7256 - vqvae_loss: 567974.2969\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 581057.8642 - reconstruction_loss: 13049.7256 - vqvae_loss: 567974.2969\n",
            "Epoch 2456/2500\n",
            "Epoch 2456/2500\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 579530.2560 - reconstruction_loss: 13067.0957 - vqvae_loss: 566512.0517\n",
            "51/51 [==============================] - 5s 97ms/step - loss: 579530.2560 - reconstruction_loss: 13067.0957 - vqvae_loss: 566512.0517\n",
            "Epoch 2457/2500\n",
            "Epoch 2457/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 569490.4880 - reconstruction_loss: 13048.3906 - vqvae_loss: 556454.4219\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 569490.4880 - reconstruction_loss: 13048.3906 - vqvae_loss: 556454.4219\n",
            "Epoch 2458/2500\n",
            "Epoch 2458/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 573253.0433 - reconstruction_loss: 13000.5186 - vqvae_loss: 560246.4207\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 573253.0433 - reconstruction_loss: 13000.5186 - vqvae_loss: 560246.4207\n",
            "Epoch 2459/2500\n",
            "Epoch 2459/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 563158.3137 - reconstruction_loss: 12999.6152 - vqvae_loss: 550229.3534\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 563158.3137 - reconstruction_loss: 12999.6152 - vqvae_loss: 550229.3534\n",
            "Epoch 2460/2500\n",
            "Epoch 2460/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 556814.6887 - reconstruction_loss: 13046.7266 - vqvae_loss: 543796.3173\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 556814.6887 - reconstruction_loss: 13046.7266 - vqvae_loss: 543796.3173\n",
            "Epoch 2461/2500\n",
            "Epoch 2461/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 560865.1202 - reconstruction_loss: 12985.9629 - vqvae_loss: 547825.7776\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 560865.1202 - reconstruction_loss: 12985.9629 - vqvae_loss: 547825.7776\n",
            "Epoch 2462/2500\n",
            "Epoch 2462/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 553128.6262 - reconstruction_loss: 12903.0400 - vqvae_loss: 540191.1983\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 553128.6262 - reconstruction_loss: 12903.0400 - vqvae_loss: 540191.1983\n",
            "Epoch 2463/2500\n",
            "Epoch 2463/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 549303.4856 - reconstruction_loss: 12923.5371 - vqvae_loss: 536330.1442\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 549303.4856 - reconstruction_loss: 12923.5371 - vqvae_loss: 536330.1442\n",
            "Epoch 2464/2500\n",
            "Epoch 2464/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 541967.7897 - reconstruction_loss: 12946.4629 - vqvae_loss: 529042.3804\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 541967.7897 - reconstruction_loss: 12946.4629 - vqvae_loss: 529042.3804\n",
            "Epoch 2465/2500\n",
            "Epoch 2465/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 541412.1839 - reconstruction_loss: 12931.3857 - vqvae_loss: 528520.4627\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 541412.1839 - reconstruction_loss: 12931.3857 - vqvae_loss: 528520.4627\n",
            "Epoch 2466/2500\n",
            "Epoch 2466/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 539999.4423 - reconstruction_loss: 12950.2119 - vqvae_loss: 526994.8570\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 539999.4423 - reconstruction_loss: 12950.2119 - vqvae_loss: 526994.8570\n",
            "Epoch 2467/2500\n",
            "Epoch 2467/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 538597.2031 - reconstruction_loss: 12932.8232 - vqvae_loss: 525697.2662\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 538597.2031 - reconstruction_loss: 12932.8232 - vqvae_loss: 525697.2662\n",
            "Epoch 2468/2500\n",
            "Epoch 2468/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 538038.3365 - reconstruction_loss: 12890.7598 - vqvae_loss: 525115.7103\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 538038.3365 - reconstruction_loss: 12890.7598 - vqvae_loss: 525115.7103\n",
            "Epoch 2469/2500\n",
            "Epoch 2469/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 531962.6430 - reconstruction_loss: 12941.3936 - vqvae_loss: 519059.5649\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 531962.6430 - reconstruction_loss: 12941.3936 - vqvae_loss: 519059.5649\n",
            "Epoch 2470/2500\n",
            "Epoch 2470/2500\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 526316.9748 - reconstruction_loss: 12959.9941 - vqvae_loss: 513367.1328\n",
            "51/51 [==============================] - 5s 92ms/step - loss: 526316.9748 - reconstruction_loss: 12959.9941 - vqvae_loss: 513367.1328\n",
            "Epoch 2471/2500\n",
            "Epoch 2471/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 526654.0379 - reconstruction_loss: 12940.8428 - vqvae_loss: 513753.4537\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 526654.0379 - reconstruction_loss: 12940.8428 - vqvae_loss: 513753.4537\n",
            "Epoch 2472/2500\n",
            "Epoch 2472/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 525066.2662 - reconstruction_loss: 12896.9082 - vqvae_loss: 512141.1040\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 525066.2662 - reconstruction_loss: 12896.9082 - vqvae_loss: 512141.1040\n",
            "Epoch 2473/2500\n",
            "Epoch 2473/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 523997.5847 - reconstruction_loss: 12858.2822 - vqvae_loss: 511057.4952\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 523997.5847 - reconstruction_loss: 12858.2822 - vqvae_loss: 511057.4952\n",
            "Epoch 2474/2500\n",
            "Epoch 2474/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 520294.7278 - reconstruction_loss: 12882.0684 - vqvae_loss: 507420.8131\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 520294.7278 - reconstruction_loss: 12882.0684 - vqvae_loss: 507420.8131\n",
            "Epoch 2475/2500\n",
            "Epoch 2475/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 519099.9940 - reconstruction_loss: 12904.4941 - vqvae_loss: 506201.3227\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 519099.9940 - reconstruction_loss: 12904.4941 - vqvae_loss: 506201.3227\n",
            "Epoch 2476/2500\n",
            "Epoch 2476/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 515853.4339 - reconstruction_loss: 12882.9824 - vqvae_loss: 503000.1797\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 515853.4339 - reconstruction_loss: 12882.9824 - vqvae_loss: 503000.1797\n",
            "Epoch 2477/2500\n",
            "Epoch 2477/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 512302.8341 - reconstruction_loss: 12840.8350 - vqvae_loss: 499443.3251\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 512302.8341 - reconstruction_loss: 12840.8350 - vqvae_loss: 499443.3251\n",
            "Epoch 2478/2500\n",
            "Epoch 2478/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 515826.5775 - reconstruction_loss: 12879.1172 - vqvae_loss: 502912.7007\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 515826.5775 - reconstruction_loss: 12879.1172 - vqvae_loss: 502912.7007\n",
            "Epoch 2479/2500\n",
            "Epoch 2479/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 511291.8876 - reconstruction_loss: 12844.3701 - vqvae_loss: 498404.1550\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 511291.8876 - reconstruction_loss: 12844.3701 - vqvae_loss: 498404.1550\n",
            "Epoch 2480/2500\n",
            "Epoch 2480/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 506712.8269 - reconstruction_loss: 12849.8604 - vqvae_loss: 493921.8678\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 506712.8269 - reconstruction_loss: 12849.8604 - vqvae_loss: 493921.8678\n",
            "Epoch 2481/2500\n",
            "Epoch 2481/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 502137.8450 - reconstruction_loss: 12876.6406 - vqvae_loss: 489323.9802\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 502137.8450 - reconstruction_loss: 12876.6406 - vqvae_loss: 489323.9802\n",
            "Epoch 2482/2500\n",
            "Epoch 2482/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 500282.5409 - reconstruction_loss: 12875.5244 - vqvae_loss: 487396.1947\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 500282.5409 - reconstruction_loss: 12875.5244 - vqvae_loss: 487396.1947\n",
            "Epoch 2483/2500\n",
            "Epoch 2483/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 505032.4231 - reconstruction_loss: 12863.5879 - vqvae_loss: 492170.3155\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 505032.4231 - reconstruction_loss: 12863.5879 - vqvae_loss: 492170.3155\n",
            "Epoch 2484/2500\n",
            "Epoch 2484/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 496573.0132 - reconstruction_loss: 12913.1885 - vqvae_loss: 483718.4669\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 496573.0132 - reconstruction_loss: 12913.1885 - vqvae_loss: 483718.4669\n",
            "Epoch 2485/2500\n",
            "Epoch 2485/2500\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 496603.7572 - reconstruction_loss: 12915.5674 - vqvae_loss: 483709.1220\n",
            "51/51 [==============================] - 5s 93ms/step - loss: 496603.7572 - reconstruction_loss: 12915.5674 - vqvae_loss: 483709.1220\n",
            "Epoch 2486/2500\n",
            "Epoch 2486/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 497125.8065 - reconstruction_loss: 12860.2207 - vqvae_loss: 484289.2782\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 497125.8065 - reconstruction_loss: 12860.2207 - vqvae_loss: 484289.2782\n",
            "Epoch 2487/2500\n",
            "Epoch 2487/2500\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 495922.0763 - reconstruction_loss: 12869.3213 - vqvae_loss: 482937.7548\n",
            "51/51 [==============================] - 5s 99ms/step - loss: 495922.0763 - reconstruction_loss: 12869.3213 - vqvae_loss: 482937.7548\n",
            "Epoch 2488/2500\n",
            "Epoch 2488/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 490463.0222 - reconstruction_loss: 12847.2227 - vqvae_loss: 477738.9922\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 490463.0222 - reconstruction_loss: 12847.2227 - vqvae_loss: 477738.9922\n",
            "Epoch 2489/2500\n",
            "Epoch 2489/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 498210.1677 - reconstruction_loss: 12821.7109 - vqvae_loss: 485296.9477\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 498210.1677 - reconstruction_loss: 12821.7109 - vqvae_loss: 485296.9477\n",
            "Epoch 2490/2500\n",
            "Epoch 2490/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 495943.3828 - reconstruction_loss: 12827.2920 - vqvae_loss: 483048.3119\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 495943.3828 - reconstruction_loss: 12827.2920 - vqvae_loss: 483048.3119\n",
            "Epoch 2491/2500\n",
            "Epoch 2491/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 495523.6280 - reconstruction_loss: 12841.9775 - vqvae_loss: 482649.2650\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 495523.6280 - reconstruction_loss: 12841.9775 - vqvae_loss: 482649.2650\n",
            "Epoch 2492/2500\n",
            "Epoch 2492/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 491767.7969 - reconstruction_loss: 12845.2949 - vqvae_loss: 478884.7572\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 491767.7969 - reconstruction_loss: 12845.2949 - vqvae_loss: 478884.7572\n",
            "Epoch 2493/2500\n",
            "Epoch 2493/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 487950.6665 - reconstruction_loss: 12778.6875 - vqvae_loss: 475209.9796\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 487950.6665 - reconstruction_loss: 12778.6875 - vqvae_loss: 475209.9796\n",
            "Epoch 2494/2500\n",
            "Epoch 2494/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 489589.6040 - reconstruction_loss: 12742.0391 - vqvae_loss: 476762.1268\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 489589.6040 - reconstruction_loss: 12742.0391 - vqvae_loss: 476762.1268\n",
            "Epoch 2495/2500\n",
            "Epoch 2495/2500\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 480362.0228 - reconstruction_loss: 12758.7529 - vqvae_loss: 467665.7927\n",
            "51/51 [==============================] - 5s 95ms/step - loss: 480362.0228 - reconstruction_loss: 12758.7529 - vqvae_loss: 467665.7927\n",
            "Epoch 2496/2500\n",
            "Epoch 2496/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 480005.5811 - reconstruction_loss: 12767.6426 - vqvae_loss: 467210.1142\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 480005.5811 - reconstruction_loss: 12767.6426 - vqvae_loss: 467210.1142\n",
            "Epoch 2497/2500\n",
            "Epoch 2497/2500\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 477492.2945 - reconstruction_loss: 12808.9727 - vqvae_loss: 464657.2025\n",
            "51/51 [==============================] - 5s 96ms/step - loss: 477492.2945 - reconstruction_loss: 12808.9727 - vqvae_loss: 464657.2025\n",
            "Epoch 2498/2500\n",
            "Epoch 2498/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 478829.3582 - reconstruction_loss: 12817.0625 - vqvae_loss: 466038.0174\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 478829.3582 - reconstruction_loss: 12817.0625 - vqvae_loss: 466038.0174\n",
            "Epoch 2499/2500\n",
            "Epoch 2499/2500\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 478990.5691 - reconstruction_loss: 12833.6387 - vqvae_loss: 466079.6100\n",
            "51/51 [==============================] - 5s 94ms/step - loss: 478990.5691 - reconstruction_loss: 12833.6387 - vqvae_loss: 466079.6100\n",
            "Epoch 2500/2500\n",
            "Epoch 2500/2500\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 476767.0312 - reconstruction_loss: 12864.3789 - vqvae_loss: 463949.7079\n",
            "51/51 [==============================] - 5s 91ms/step - loss: 476767.0312 - reconstruction_loss: 12864.3789 - vqvae_loss: 463949.7079\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3af6d45e50>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3af6d45e50>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "vqvae_trainer = VQVAETrainer(data_variance, latent_dim=16, num_embeddings=128)\n",
        "vqvae_trainer.compile(optimizer=keras.optimizers.Adam())\n",
        "vqvae_trainer.fit(tf.convert_to_tensor(X),tf.convert_to_tensor(y), epochs=2500, batch_size=128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8uBL83JsEW-H"
      },
      "source": [
        "## Reconstruction results on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "Ao1lzo0vEW-H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cba12022-56a2-4588-88dd-644b245475f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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yjGS53W5pmoau63DOMQwDfd9PkS5wo757jDHCfdnjnxXWWuAq6YIQY5jOaY1N0a5zL7z39AXyBq+yGUlACpHgPX3X46MHLYilgRIwNumvL5Ecoka8D+z3Ld0QGaKw9wVl9AQdsCZgTcQYT28CVWGpygLrCoZo2QdLvTjFFQVF6Yh+wABlaVO97S1HhsZaqnoFckEIjrZTbPBYq0T1uNLgSgMaCD4yqOew36admkaaduCw39A1O5r9BmsNdV2AGMRarCsxdvwSATUlgYLeK+XdfExvDW806V5cXNA0DU3TAImcvPc8ePCAsiw/9rW73Y7dbseTJ0+ukK6IYK19IYE2ErQxBuccVVVNj9usE42keNvyQghJB0uJOou1DmMiMXqiRkQl32f5fKUMZhyj3T2nbw7Efsdmv2V3AbvnBb331FXFB+8/xp48Rorqhdfudxs2F+ccmqf0IeJViFoRhgIvBYML9OqJYWDrB2LwhDCgYhmCcBiUs4ePKauKqkzJtrooWK9q3MkKZ293TVel4YNvWvLRjxcEL3RNT/AD6iNeCmIsIDqcGAyCRWjaSIwhVThEy9ApoQuoF5yznCxKivqU1fqUBw8fcnJyhhHLMKRdhLUFQsmb/hl57aQ76qVwSX4jdrvdRLp1XU+R6unp6UuPF2Ok6zp2ux2bzWaKdPu+Z7lcTsR7LBW0bXulqsFaSwhhkjSMMa8UXX9ahAAxpCJzAGNIZZFisVk2MFfkgzd7Ub1TOI6qcgUKRPrB44cBBIZ+YOg7DufndM2B5rBl3x6IIeT2Ws+iKtHDlocf9FSrM4qTh1fWm+97+ral7zt6HxmC0g0Ng3MMhSM0B2IYiKGna9qJdH2IRBWiGEoiZr1m+eABJ4sVi0VNVVbYWyZcIAc3sKhLTtZLePww7Rg1EoCz0yXr9YL3HqyxNmnA9WpJ1JAreUoWleWkNizE4wrh7GxJvf4C1XLN+uwBVb0kRkPcRlwhuMJS18kL4k3GayXdkfQOh0Pq1zaGvutyzaBMhNk0DYvFYop0vfdXMsDXj7ff79lut2w2GzabDW3bXpElxkh3lCD2+z3e+ymKdc5NFQ7HpHvbEe6IGATvSYmzCMYo1pF62c2s175u3CQraXog+wrkx4cIIYJE2v2B5tCAgWa/Z7fZst/u6Zo9h8OW4DsGP9C2LaVVqsLRPX+C1cDp42+iWJ+hmuuhdCTdhqEb6PuB3gf2+w5nLYUzNOKIcSAMLbt9QwwejYGubTHGUNYlD5cOKQILe8Kj0wWr1QrkFZ1qPiUWi4oHZ2tOl18gRghRaYae05Oak2XNNz06w1mwVmCxzPGEAAsY1ug3nfJ4VQCBsrIszz7AVgvsYgVYug6aQ6RcGspSqKs3P5n82iPdqJHnz5+l5JQxdO0Y9TJJA03TTBHoKC+oKuv1+oXjee95+vQpT58+5eLigqdPn6KqWGsnvTTGSN/3U5T79OnTichH0m3blvV6TVEUqCpVVaGqk7Z8m7AGVITBC1HAWKgXIGaOad8U7A6ewaftr2LxfuD5R1+lefpj9M2OoIqRpMvawhBDqle1pWMYPF3X0zYN0Xt88HS9R4CiWmIsiDWoOJ5dbGl9QH2XKhuM4fne8/TZUzbbDTF0eD8wDAHVQFQhqEGM4MoF1XLN6eMqdanFgdD3OFewWJ/wpS99yIOHD/jgww+x7m7JdsSj9x/w8PEZVfFBukMhohgRjMi1KPva9bgabMl7P/kUyGWdxubrTs+1FhYrQ7WAuyihvwvc2WWOJOacm4juWDo4rn/d7fYgqXTKDz5trRG89/R9z+Fw4HA4YK1lGAZ2ux1lWbJer69UEozP3W637HY79vv9VLVwXJMbQpgkB2MM2+32yvWOxDpe7xgdj7jtqNNYwSqYPmu7kqpnUvR/q6ea8Q0iFx3QtAP9EEiUofhhyG5bB7r9DhVwxmHEYRxYSY0JSmpx7ftA7wPBB2J23pLcWhujEkXw0dD2AeMGmkODagBj6NvA0HcMw8DgPTEG0NTBON5iAGNSQrV0VbrGaLDqsM6lxJMpsLak+IR8yG3CGkvUtI6zkRgpTcwnm/GMEtvHlCKIgapKf7/h+bMJt066I5l2XTeR2HEVwkioo4Yagmez2WCtTcmr/Dgk0uv7nv1+z36/xxhD3/dsNhvKsuS99967QoBN07DZbLi4uODi4oLtdkvXdZRliXPuShnadrtNWqkxXFxcTKRbVRVFURBDIHg/HdveYWuiK9JidBa6mFzwQri6kOaI9/UhKuwPLW3ncYUFVfzg6fpA0wbaJmAcBOuwAhIilTNYZzCZEIceeq+EQQl9oCzAIEgQomjymPVCN0SkD+zbgNcWMeAHmWS1vu+TpaOARkGNoCqEIIl0FYwWIEnmCGIRLNHD0Cve3zM1KWhQugh1lXZxAbC3tKKtgUUNbVZ2is9BkHInpDsMA9vtFlVltVrRNM1EnCMpH2uqo3zgvX+BRPs+eREcDoeJdJ89ewbAarVisVhQliVVVbHdbnny5AlPnjxhs9lMVQ/jubz3dF2KGOCyVOzZs2eTdDCSsrUWf0fNEDfBCFRlItkQ04fUWN7oesN3AZr/GPqOtu2QzlLVFWI8dR04VJ6h9yCKKw2FU7zvSaK8JWIQq9hKCUOPmoAtA9aGvMUOEH0q8ncgtsCYiHGkVisRsAYfA70fCDESNbl7mUJwLn1pOwNloVSVsDwNaMzJPBtR9QQG2vacrr3fPbgCQ4gcmoZBS6rSURdy60GEz1Yln4dZOLf+GxirB9q2ne4bhoGu615KuiPZpqaAyzrFYRjwOdoca2VDCBwOB8qyZLfb5e4tjzGGw+HAbrfjcDjQ9z3e+ylpFmOkaZqJeIEpSTYMw/ScUa5w1qbWwtv+Ab0Mkj6nZkjbr1QMn25GJu+bGfcMQZN7V+yxoQNTUNkCY4S4qKgWC7wf8DGgxjGo0A2eIUQ6n2qrh6D0Xul92sIYUvOLoAiRqErQiOk9OE9kwG4brFHECB5H0/b0fUhShGq2VFRMFCSk7ZEYQbxP50EJCD7mNnNlMk26D2iMRB85bA8cmo7NdsvQL1ksa1gtcS51VibJ4bMvbCfvsLwQQphqZMdf8Ki1bjabK73n15NacPUX0LbtRIgjMasqu90OgAcPHjAMw1RTO0oLm83mSsvuSKgXFxfTF8CYXDuWHdq2pW1bRCSVlxXF1Mt91xADUoJ0IEk6zNneyxKyGfcPETAolTZEPWCk5rSscWVBZR/QtR3RFLTdQN97+n5gu/WQv7yLwhEUQlDadgCNGJSisKAQYsy+AcqhHVj6DmcDz84HRPsU6FYLuqFj6Ae8T560ESX5gEesTwvGOqUcIJgeY5MTXTek45eFZbFcs1gsP/E93wY0RPyh5fnXf5TziwuePn3Gg/cesDo9ZfjChyyXS+qqZFF9vEvZq6K+RSa7qQfqNgOeOyHd7XbLdrudElIj6T5//vxKZ9elz4CfIt7jrXzbti+YzIgITdOgqjx79oymaaiqihgjz58/5/z8/IUOstEhbJQwYnbvuuz+uloW5Jzj5OTk3qKCYwSfpAXnkhYoJhOvze30M14DhD4nwwqjhKDYmFpo6+UqRbf+nKgd3vcMPpVrqUa6XqYPcRjXt7VJlwUwFiVV7riixEtq23UI4FJZ2qHBR0+IgaKs8CHJB9bGZIIUAyCEPjAMHSG74bmiwPsKZysKd0pdPqIqb/YXufWfmDEUVUE0KzwB41oiSwZfs9/XqFpCCsgpizev8qDX/FlsYLW63eu7E013dPIaCTTGOCXSbiLdMZINIVwtCM/Jret1kqMfw+FwmCSHqqquaMDH5xgj5OOyMWC6//icY8nY2BhxX1AFIgwD9H3OSttcyQAUR5UMs8xwj1AgKl3b0zYdUWySp0RR41GNeY0GYvBE3wPJWFxjJBJBRz8CnYzG8x0IguroGpfWY9SYayTGygRNWi5xmjM2yk3jXDRU8vOVoe9SRG1AtMSIpXAVdV1f6bS8S4iQrRprjAlYd8CYGpGKGGxqaghC1+c8BlDaN2dtT21Id3A9t066x5MNxn+PVQijBjviWEe9bmN4ky3jeNwxWbfZbCb9eNR0R+ng2MpxrL8dvxCOSfdYghifV5YlJycnuPuMdBXiAE0Dh0Mas1JEcP7SK8rOXcD3j6gwDGwvNlxsNtSZcOtFRVErbbOjaxrCcMD3e/zQYk1ixqCKSMzJOME5kyc6GEQURdEISkSjEnzykzUYvBoS9YIPgkqSFMIQUCIQ81JI/1Y1yVvWCNF71ICo4CwsKsvpasHZgyWr1f2Q7lgjZt0ZRVlRVYGiXOJcjaHAGUEEdi3YCC7A49WbsbxFoJT0GYzF7efm7iSRNm7nR1hrpyaD67fjqFRVJ9F/xPXE23iOMToezzc6iL3gQcslub9wrhuuZb1ec3Z2xtnpaSogvyfECO0BmnzzRZqaaodEuilxAqsX+0Fm3CkixJ6yVMpCCbGhbQwhlLje0B46+nagbRr8MBBzUmskwxhzbbqmelwxqVg1RJ+0TAwheCJymbg1hqIUbCZdi6Ufkom3Shp/ozEgpDpgzZN2Q9Q0YbeoiSopox8CsYwpYWXhFYZI3B5MavLxg0A0WJtI1TpSByapIU7HH9kbiLvY696JvDBWHIyR7piwGh8f/74e5QIvkO7xca9HwaO0MCbjbtJnr5P19fuvOntBXdcsFguKsrx3eWEYsrww5G2lSVFA5FLTXerl80XenO3Y24uk+5SFpaosai1lYXBWsUScKE4Uq5rNtFOEq7nKIK2x1MurUZGpUShmrVfx03yzVNeLKsF5xg+CSpqdd7xWY07AHX8mQkx6s3OkTrWYjj9JaIb7K6nK69IVgsujeY7r5IMajDdUK1Jts0kSA/d4iS+DHv2tfA4iXVWdOr3GRFpZltR1PT1+hfA0Ju/iMcod5yjkhTpiTLJdcZbPke6YhBtJfNRxR4z/v/5lMC2ArCVbazk9PeXk5OTeqhZGaIS+g66FtgFvE+HaIv2SYr49PEsjTEKEe2wseochgOFktaB0UK9WrJc1hRW033PulF0Rkc6xi55DDPhOCT7Se00dWTERojFCCDF1lQWf1yqoegRhMKmqwRil7TsETc1CJmnEUaHv844tKmI0Vz7Y1KIclBCTiXhicUsQw6Dpy+J1bN6LyuGGyHBu8UNAtaXzliEuUVPyuID1AoolNECVb68bgfQ56yMs7e1uEO5E0x0NY8ZIsSgKyqPI8Up0q1eTADlFcPTvhJF0j4/R9/3UyHBTq/H4vOteuTdJDCPW6zWr1ereDWashZMTqM9TpGvGut38Czfm2H0M5PNSlPh5hzFQLlg/eszS91TLNWW9SJ4BsaGVZwy6oawOuD7ghoC1DlcIUdLuyViwSk7mutSlaVPnpYiZJIiUYMu7NTSvZcVacqSbjGFEDCqCdZdUUOZhpWIK1qePLtt+OePs9CFnD74Z6+5/koRYwRYFRf0YSd3LoI5usHQDHJq09q1LO7kPvgBf/AKsyldoE74rKHQBBg9DC/U6Xd9t4U5Ey3HCw4hRXjj2P7i83VQXd0y8Cdf12PG+MXq93txwnWTHxN71SojxOsbH7zPDewwxUNZQVlB2Ob7KkoLR9NmffD509mS4N4gBKxRliTpDtahx1SpNrI0G63apmyz7eygpmTUW/o/z75JqlszCjTVYbG5Dt8SYfqlGTPLAVSV5K6QaBqNxOo4RQAQ15ADk8vjGOmxRUlULrC0x1lHJmuXihOVi/VpIzDpwhcGVC0zMMVZyvCSElMdgfBcWVkt47z1Y3f+lXsFYTaSXKs+t4U4i3eNqAGAykXHOXSnpunzOZS1jCuGu2jUCkwRwUyXDSLjXbR+PX398PdfNb0bSHetzV6vX8CsXMDWsT3NEGyGYpLLokKOBHNiY2X3s3tF3B2LosaXFlDU2KrrbMTQHhi6NFu/GUVDEJA2oIhLS2s7VCyLJH2Ey14qKjLJajBhRlCQVWJM+C6KavBRU0SgY0fSlO94/VS9EnI10TUtVRsq64tHK8WBhWRd5Xd0z6iqtYbdIxjROQHoglSvTFymwEJvq0itH2ttfpYF7x8qBZp35tv157yTSHV29jqPIsYLhuoE4CM4WhCjXs7kAACAASURBVHhZypXzCxMRH2/1XzZW53oUffz/68+77sELKWqo65rVajXpz/eJ8ZKWy5RY6JtL0hUHtrwkXZhJ975hyxWEkkiRSkkIWW+VbMvpJ6+OqIGoI5EGFAFNeq61AmKRsTbXQAi5gw2Daq5Nh0l2sKZAdRzdlIaVoprniUVGxzEfPLET6kXaColxiC3BFuhr2hlZk0xoygIKm9a2KcilcKmKAc8oneMMrOrXmyAez6035/Q/M+4s0j1OVgFXjMCPpQUhDV1U1ano+zLsvZlcb6rlPdZ6rz92TMLXjzM+b3Q5K8vypZ650zF12jS+8IXwWVFWeQsZIcjlF75xl6Q7VyzcP2xRoRh8FEKOTpU0kUHFpHKt/CHVmEgwHq2vFJQqYlIb8GRwqLmsTDXV7ma9bYx/hfzHWAFBGlCpGhHJxJ5fE0MkxEC9TCY5YguMK5DCJgOd17Busl8Pde46s5Jl8jIRsegluRmXTJ+q1zPE+EWMct4t484SaSPBjm5jIkJVVddaa/NCnb5RxoqFI7mBS7IbCf36kMhxqu/LNNsR45fBcbQ7vqYoCs7Ozj5xLE/InXNjRcRoIiK3tHcrKijKZFfX++ye5MGUOdkwE+5rQekshIGLzY4YDdYWhMFwiBWdLFCpENsjJuB7zzBEBq9Zs03VCyJgYyREjzVmSpYFnxNpZlyjafhikaeGGOsmjbdt8wiekAYAjNUL3kfIskNRVhTVClufsjxdcfaw4r0vvJ6fmwClgw/eu3r/6Vn6W6c/mJLEbwrEQHEHm947Id2iKCb7xKZpJlOZkaBGM/KxTvFVY/jrZDh2kFlrp8qGm6SDTzrmeG3L5fKl491Hsj2epzaRrnOU2Rzns9b2Tqe2Sf8yDrTMOu6cPHttsM5RaM3qpKBaLLDGECtH0/W4rqOsa6rcFORjIOKJhNT6a1IyNMaUlxBgbP01kgzMRcjrOAUWxrpUpWIMhSsJ0RO9T8k5axCSV3WKhBUnqfvBGIuzHisdwp7VqmNRlzeSWYhw8CAxIFGJGiDH4cEHnAFnhXpRfupd3BSpX7//DSLXl+GurvHOSHcs4+r7/kq77Ui6qXsslc1M1PgKb/L6L/+YyI8j2Jsi3uMo97os4JxjsVi8lDR9CAx9P5ntHL/O5qi3OLq+zyQ1CJATZ7PHzZsBIxbnhNrWlJWbdmzFZptLxEqKosQXHlcM+AgujrXngIJHMUJOlo17u2w+Lqk9eAwerLO5+cUk4/QhoEZyNJwE0DTqJu0MTa7LNcZiJGCkw4phufBUdbzxPQWF3QB2SJNRYwgglojg+57SQOmEoiyOzjvjs+LO5IXjiRHHXWrOOcqyzDW28cZoVD8hdXlMemOCbvy7KAr8MLyUfMfXMZ4hE3BZlpydnb3UWWy/33M4HGgz6Y5XuFgsKKuKoe+pFwsWdX2v7cMz7gc+BPp+YH8YePDodFonqT48gBGiRnzwyRtBb6heyG2w4/0pWSwULk97NmmCrhjNGfM0/UFDKmERPKIeaxQrR1UNpK43i+CcEnyHwXBSV5w+yh62NyAE2G5AuwYNPc4ZwKIqaTTQ4NGoPPSWBycFZ6s3RWz9fOPOSHfcpo+lXsd1u5M2qzrWgr+AlwkDo6QAl/W/4/leJg0cn/fK43mrVxQFVVVN8sIxxmi9yXPauq7LiRIFkSQpWDuN9hFg+RqaK2bcLUIsCCH5GYxTm8e4ICW3IiHfYghZPkhlYElFM6iGpNHKWHUAqjmxLGDEkap5SRru5CqW2obTCPPxmIoVm5NoIR0nez0M/YCVNafrh1hbI3Lzx9waWC9h0w70fYcPFucKrDE4GxgGj4/KdttQFbCqi2nO2YxPjzuVF8bW3GNvg+PnGRHCEb2OSVrJVnh67fnT60bSNQaXpQVjzDRhVOTlivz1Jgnhsk15eYO8MJqbj8Mxh2Eg5sU/RshFUdC2bbpeERbL5Y0VEzM+v4haEKIlRIhBiCEiU6eYZlLM/gd5pE56LGYaTYQoOTmmOXs8dpqppikRl62GYxVDqlCL2VdBVVLFGqNEMQYAZPObSBw8hRScrR9hTMXLPubGKKuFsok9fdemErMaTGmxzmNMShjv9w3rZYEPYwQ+r+vPglsnXWstq9WKzWYzteWOZWL2WqLphTbc43IzYyH3nqeHZPK6nchML6c/jK3GVZVMnscFe+zJcEXPVcXk167Xa05PTm6UF8aR7ucXF7RNk8bEd93Uljx2sJ2fn1PXNX4YWC0WFHnA5Yy3A4WDYBXtBjYf9extwBQtT56cc77d0/TQBcMQHUGL1B6Rv/yn6hxJlQkYh8WlEsmYHUfEohTZb0FwuZnIGINXwRiHsQNis6tYjKmjYOxcE0PpHFXpOK0XPDo95eHpg4+dpBuGge2PPePJV7/Cbndg/eARvq8oCkdZG7ouEoKwXC0IwXKx9bz/0M2R7mfEnUS6xyPP4VJXPa4yGGeXQXq+AkZjsvRRk27XSPdKO3Ema+tcinaNSf92jsK5K3W0Y5Q9vlZVwVqctRRlyaKuqeuasnwxSxtCmEa5D3nmWtd19H2Py1UYPhuqA1PUK0eGPzM+75Dkn2AU1NM2LaoDuIbddsdhf8APQ6pFL0tiGHL5kyRZIRd8xpiHUdoUUIhqbu8WrHNU9QJr0jqv6moquwRD36fRVX3v8b5PdpAhZqkraR1FUVBWFQ8fPeLkwRnlcpGi52tQVQ7bLc2hodlvk8dDbvAAiNFjXEkIFu8t7QFiH+n3gdo56jp1l834dLgz0r2pfGusMhgjUxBEQiZJSXqVSd07RDNttcbo1FwjMlWdNN1J383JtJF0xygbmDx9R1s859yk5dbZzvEYY5vwOMp9jNq7rqNpmikh2A8D+/1+khsOTYMtCha3/cOd8VogpE4pbxUjkUO7p+8bounYbTc0TTI2N0aoqkS6xgjBDKjaVHcrkuf8JQvH0Vdh9ExwZclytcqVEI7FYoUrLNak4KDrO4YhTa/ouoa+7xjahhiFqDk3UTrqRc3j97/A2XuPKNYvrsBxU7l9/pzddsuhG3JuoiT4NAbIRIOrLN47/ODo24iEgBXDOnfIz6T76XFnafZRNhjrdeGycqEoirwADTFECleg2JQMEI/GcRJumIh2JPO6ro9qHXWSFMqypCpL6qpCQ0hbO65WLxwbqZusPVdVxenpKcvF4oXrH5NoFxcX9H0/vYdjSWQ0UT8/P0dEWCwWbDYbilwNMeMtgYWyLnj/mx4R8ejBMARDUQ4UUfBDk4g0BGLoCUOH7wc0e4kIButkWrticpJMleWipCgryrLCOosrCuq6Tp69ZpxMbbHiGPoeIxFRT/Ad5N41QbBGqQrDsq4oP2aXpQoXm4btxYGAoR8sPqaRUFFBgzIMPTEm57KikFRZoeZeLXnfVtwZ6Y7R7DjVYXTwWiwWk8lM3w94PM7Zqe0xhEjMs6TGCHd8/RjVTsmwcQBfPldVVclQJ4SpKOKYdMscyV4n3cViMT12jDGiHWtzx2h6/HuUSq7Pgmvblq7rGIbhqgY94/OJUeKyQlFZTs5WlLXFhwKvilhDc8jWoiYQvOAHxftcxZC7xowVBAvmaF3m+lxXZFOooqAoCxaLBdakVl9RwUaLtSERsDGIpHbjJMEpYHOZWq56eEn9T4yBtmvpuj1d3yBFkRoqRCaDHQT8YIjRo+pQhnTdMsoh9/Azf4txZ6Rb1/VkLj5qpcdNEgAih6kFNxXGGKJ6ROOUHx1JbSS78QZAfm1ZllMFwuinm4xHri68Y83W5mNWVcVqtbrRzrFpGna7HYfDYarIGM81+viO0btzjhgjXdcRQ2C1WtG1LXYuH3trIAZsDY+qE1RX4BswgischlQq1g8DfW8YOghDJMYhNS1YsC4ZEdgspSV5TZK84JIeW5YVZVWxOjnF4EE9Q+8x0U/Sw5jDSDuuRLDWuml3FtXn7rIX4f3AdnNO02zohwOlW+DKEis2NW3ktvyuA8QiZCNntRhxiM0OZ3MFw6fGnZHuyclJ2iIdRXqLxWLq/Bq36V3Xs1ot8WGgH7rL6gK52aRmijLzY6O0YK2lyO24oSyTPsxVv4VjYrXGUDhHVdecnZ6yXC5feA+Hw4HNZsNms+H9999nuVxO5xoTcmNSsCxLYowcDgckX9d6vab+mC63GZ9TKKiP9Ls9zWZDc7HFHw6o79G+J+48eI9RP+UXVCNOHSbbaRlTgZjUQeYWiK1ThYKY9JwQ0ezsrRoRNQhCWZTEeokghL5HoweN2KJmsVyzXJ9iVw8w5YvrGaBte776lR/n+cUOPwyorQiEPGk4eUIYY3FuhZgKsRXWVRy2nvPdBe7LkS9+WLFa378T39uCOyPdMRpdLBZX9M+RqIZhYLvdpsmoeXJp0rtSpldu2B6NybSpEYLLioiJALPckCYA6ZX24OMknDWGMssLY+XCdfTZH3UYM9P5+cdlZaPsMZKuqtL1PU3b0rTtK/k/zPh8IQwDcUgJqLpesTpJIz10aBiMZVGH1EBBgbGGoInUysphXIm4Ko+clGyAnpyMNGanMO/p+y5VOUgygRIxuEzCac0VWAFiSEbnrqRerFidnFAvVrgbksJ929Ae9jTNISXUxBBCxKvPDRwRU6Qyt7KqceUS62q8N4i0qHZErdG7o403C6MZXJ7Yclu4s5/eGJWu1+srs8tGGUBVpygyxJCt6mCyU5v+c/WYxwY3I+na7DxmcvQaynJq1X0Z6bps5bhcLFit1y/VdNu2nbrqiqLg5OSEZ8+eXSmDG9+Tz6Y4h8OB/W7HbrebSfctxLDfE/1Asax474MHnHjh/PkTQrOhPxzYNGcoHUhHP/SE0BNiT70scUWJLWqGMI7oATFJj01TIwIheEKMGJf8FRZViXMWnKU0brQ8x+j7l+UIxlFUFfViydnDBxQ3tLNvnj3l4vk5fbvHOodYiw8eH5L5lOSgpbCO5XrF+uwBZb3kyY83uCLdFqubA5S3EhHoSUPbPg+kO2KMSo/LtsZt/m63o2kaYlSG4dLf7ajQLN0jckXPndqMjyLZvusmknVHdbrHibfjxWIziRZliSuKG/0Suq5LkwBEptHsda7pXSwWxBiniRhVVU2z4cbXjg0iM94yuAqRAreoMRgqFZaL99D4AN8Hot3yfHvBZr8BzlOnWPT0vSdEwalF83gEVaGscz25pnIyjEGMpSwthbNUZUnOLGPM5ZjS6OMUnBjrKKuSui5xIjdWGIQY8MHTD6nLLGpksSqZ3MnyZ7OqKlZLy7IWilpY1AWNs4gKzSHQ9+/Imo7AABTcasnGnZLuywy+jTEsFguWywWr1ZJhCIikzKo1dnSCRtVcIdsrzRVTMm6segjT/42kbqBjOWL89/VEWnHkE3Edx5OCxyqH46qF8b0cl7WN1+BDoG2aK+OAZrwdMM6hVsFYbP69OleCQigijx5DdBG1EWM6hh58D4MfUpVOvKww0FxjO7p4GZvXvEs5iqJIRk7HpCsSESJe4pX7y6KgLIsUPd+w3GI0xGiSh4RPVQ82O5Ylki8nua2qSorS4ixHnyGT2psvDbDf8nxaqga5bX3htYgzY/nYyekpXdfT9z2HwwFr7VTxEELAeItzlxUDo4vY2Kkz/hhiJrYx6rXWTnW6432jLDG1E9sUQVRV9VJCnK7DmNQqfHo6TTU+TqI551gul5PX7ug7sd3tGIaBEAJudh57a1Askkx107qxzvDFn7BidWZ4sCk5fyZ0XUPXHnj6bNz5COqzd4IIRsBZQ1E5iiLlJKq6ZFEXKTlc1kfkmka0C8rgcoVCJt26rlgs6peWdAVf4IcS9SUaS6yNrOqCQIGYIkkKJ6cslmtOTtbY0k0RnhjBFgZbRoyJl1rnW40AtMCC2zRZfW1MICIsF0sePXpE3/eTh8FocO69p23bKwms4/KwsU530otVMdn5y+bKCI5qfI8j3mPzcTsaQX/CtY7ywnHH3dj8MUoQcDmeKFnjDWw2G6y1nJyc3PFPdMZ94aW7lrGeF6GsKtZrw+bZE0wIWO+RPqB+QHVgNL3xMTAUoCHiB/BVibWOvgsMXYlzBa7wGJt2WJonUIDmIa9p7dsxMDhTHpzUUwR+jEcP1hQosTmwLyOYwMOTBYfBEjCU1mK4HMynmKz1Stppx8ih6bjYtDypDjx8XGdP37cTPkLXpyZuW0C5uJ0a5dcafpVlyWq1mqJHSFv6kbCOE2DHtzFqDEcF5uPfxy5ixyVj10n3SmPDS36SxwY5xxUKx/PYxsh8xHGlhveepmlez3ThGa8VzlmqCpbLBUY9JgwUtoAYCWG4HEylqckhOdel7s1gA34ISW6zA9YNKTgQc5SYVYaun4ZhpjFWirGOdV0iDsw1QqwWJdEveHB2Sl0KYgKrk4p48PThcqR7jOkzKFGIKH4YCKEhhIaujex3cFEIZ4++8Lkj3XE24xjYjbvVwuVKEU0GRSEkSajrLaXJ/lu3hNdKuqNoD6m0rK7rqYttGAYWi8VEeMfeCxOpHkW6x1MpjMiVkrHrmu71xNzLcGywM772qnl1nEj3+mDMsTtts9nMpPsOwllwC8NP/tZv4bDZsn12Tuef0TY7+m6Lj2nqbyTi3Dgip8f7Lq83R9uM69Wm4FMEa9ykq2qI9EPy/iAatidnHPYd67JiuapYLC8JUUSwi5JlUfJheZa642yEoke/9hG73QHQNJYqHuj6Pl1TVLYXzzhsNvhmQ+c9fufoLyq++Sf+LIri8yebtb3n6UdPefrkIxbrNWcnJzx+eIYtCmIA38FuB0Nn8YcFi1UapHlbeK0/sWNf2+P6175POu/4/2PiG5sqYowM5tJIZEx4wTjs7kWznPF4x5684WOqC65bUcIl2Y5mO+O11HU9abrp9MmfYXQo+7xg6NNYbGfTt7uxyuDTBAE0TnPgPu7Lasa4ecoNPMsF1jm+rTxlt2+42G7R2BE1EGLA2NQ92Q8gY3IMO8lmQ4BLt3+L5HE/waf63KIcELXU9QJjHSFa4g0z19PnIxIJOGMwRsGZZOVYlqkD2CSDHuMMIYIPEefS1AtrAGsRY4k4oocYPn0UeMUv+9Md4hs+X1Rls2m52DRcbFu8OKyxlK7g7GyFRksMgsY0o3BRCUWRXDRvC6/9a2okyrHNd6wQGAltfAyYSDfm7dhoejPKA1M3Gy8aoMu154xVD2MS7vp04fF81ysbxmqE4+ep6vSlcXwsVaVt28ks57XhuFb46L2Mo7uZmlLAe6XrlOjSVGIx5LEtyYHqeHfwSZM6ZiSkksSCyi0plwtMVYF2aEykK+KJqnRDMhYHUE01tCEoTZ9GQitKjAaRNK7de9BYEKNH1FEUqVsyaIpQr6tvI0QiYpJdJZKreAoHkqoYxBicE4YI+JBnt6VZbqmnIlUHpVHw2a/hG0TUI/P2XHF007XeBsbVH6PiA7RdT9N2tG2HqwraoqCrOsKqBhVCtPgQMBGMCmLMjRaZnxavnXSvY71eE0JIfrWZbI8/5CPphhDou46qqiaTmZHwQoyT3+4YeY6vGUlj0mFVL53Lrv3GrzRiiExtvsCk745Rc13X0zDOY3P2zWZD0zT39NP7BqFJr7PO5QJ9aJrAxSZQFZHVSlgsYLvdEUNqNx3lnqqqWK3Xc8T7jaCEdVmyfnDzXvUofgBgf4j0feDQ7YmhJ4aBfTtM5WUx5s5NUYQidZf5SNfvKaoFHvfCB9xYpVgETCZZSCPbqzQvCCNpvZelYwjQ+8h+u8G5BmOT+Y21inWRyEDUAss37hvdD4HeR/rBU9ep1O2u3ae7AQ5tpOu39P2OrtvhDoa6sAy+5LBfghgGb9nuGrSP7AfDFx8vsZW9tXD8jSLd4wjzOLl2HKWa3H02EZ5NRd3G2qTxZiLxPnX1HBPvpAXnCC/mf3ddl7LLcjTf7VoCbnz9mMgzxkzEfb0W+BhN03A4HGiahqqq7tyHQVWn6H38ojjWvcdWac1fQk3bZh3cELyy3Q5st56uiLmeE7a5a1A0UmT7zJsMgma8HJdkevMndyRc71NEZoBmH2g7j4pibAni8DFArmBIr8nHC37aAZ4/f0ZzqDjsDzx48ICydJQFHDoYek/fHDhZpym/zll8FIYgaADVAAS6IRJV8CHiAwz5FoaIwTB4ZX/okrzxDZj1ex949nzHxcU5zX5PeziwWNZUiwXr9Rn1YkG9qJE8qNMYpTsMU2Tcti3ee3zwNPsdAEVVJy9YwDibfz5CtVgCKTHW+0DbpA7T/e4pz5484fzJE/r9gX67oTm/oCw3mGKJKU6IUVAP9ML6UKPOUi9vh3ffKNIdcewsdh1jcuyYSEbNdyQYk2saORrVA1yJQuFy29EPQxqjDqnMDKaSm2M5ArisEc7nG4n2xqnGqpN/Q9u233D75BXZ4yV7Rbl23zR5OX+KQ/4ZhBCmKRtmrIcehuSEZpOON/TKft+z3/f4IteTKuwPhzTfSyOLrGXfyT7wHcZIomkGW/riPOx6mq7HugZXFkSg63piHmp5/DuQcRhmDPiup3VF2j4XC5YxeTe0HXSdp9l3yW83d9oPPtANIQcoo9+0SX1vIdL3nmEI+CHie48oFMbSHFqqqkQXFdkR8sZ1oXnmYdTIMHguLnY8e/KM3eaCbr+lWiyol0u6h5H1SSBEELGITXLLftOknwmw3x2SmXvfsb84R1Hq1TqVGwC2KNCc31menIIaYoSmH+ibA33X0PdbdhcbDrsdcRgY2pZu3yBuwFUnVOtIVa8QtUQvdINS+xfe1qfGG0m6H4djd6+6Tk5HYzvucRmIK4qpoyzkspox0TUm3kbybpsGk+t5ww265XECbrR4BK5UTex2uzTSJ4+cH8mybVv2+z3b7Zb1N7gdH/qe4H0ao5KPd1yBQdbCxi+eUZYZjXaMpM64sQxv7MBbq9JlrXm3203dRkOvXFz0XGx6yiIyDELfw8XFNs2UM6k/v/yYhpIZnx4iUJUwdAPnH33ER1/bcLE50A9PQNKwy12buxyj4o5M+csx2SzgbIWxBaaoiGGgXtQslgusK/MXsGd/2NN1B6Iqzy52NG2fCdfmiLLHDx3e9/TdhrZpaJuGvmtxAq0Tnjw8wYT3OS0Fu7BgHFBydcR3GsXlgd3uwGF/YL87Zxg6FMWVNUNQht2Btvs6z589oSgLRMGHIZ0/WwVEwJgqHS94NHgE6Npdao9WwUcPY57mx77C0Ee8V4YYEVWEiEqH73usczR54rH3kXK1oCpKSh2wNlK6knp5wnLhuM3JW5870oUXi9NHOWIk0ZF8R7I57nI7nkw8RoVTpCuXnr9jW/H1ZNpI8Mvlcuo0G897HVOVRZ6hdjgcqLJsMs2RG9/LkQ481v/2fT/NYNMbSFckifzjfLjxNe0NpNt1XW4rTVJM17b0w5DkhUy6fhgrR4bU8TQI/39759rbuJWk4edcSEqyfOueSbBf9///qMUCgw0GmL5ZFkWe236oc2ha4ySz2W6mJ6kHcLdEy6Ik0y+Ldd6q8gHZP1Iy2ux8MUZt5vMNMAac9xwf73l4dmB7/v7hRCkTpWRk0HqRf1OqE4BlUc1UW1kIFmMLNhbGccJayzBUJ04R/65UthkMWW47h7FQiqcUS9cb2uGZk8M7i3eW5CwlZ2LKhJjkK0SMjVgTwITFP2+sJWOI2TBGOD8/M55HpnEizIkYIcwWbC3AKBINlzxDsaS60NjcBDI5WXKrjkIu4na2GMgeMC/bi/QyDqEwz+LYWKZsGNEBClJQlQspFLwpeCtTPqwBZw2DdzgM9ise6v+WonvNOtfa0g/rEt4W/TYxbeILLwUOyyrqqmMZvIju+rHee3a7HfM8r4YHvnYzNJrojuMoYlinXXRdJ+PiW463tAbUVahXbSXDG6Lbotwmut57Uh0vtIiutaQYiSkxXS7EriOmhO+6RXSX9IJ1pADzHJhDwJpCiIYuINOVKRJJ1M91XRCifF2cdxzubnl458F0fHj+WFMOGe/FMpZydZ6IP1KmCtdKtRgjxhZcluksw+ChdHUhVARXXApGxgF5h0++jnbvKMXVfKqIbqrl+N45orX1GC2kumidUqLMqe7fEnOh1IGzAXFBnCfDOF5qtDwTYyKlQohgnMFiZMCxydhaOZJLDZDakNraB8FgxFqHvF9TCq0/gjEWTE3xtiKHBNTxSJSCseLEMaVgTf35nHG24EyRxu1Sj4czUsDyM4M4fhN/CNFd07qJ9bW9YxOIEIKMZ1+Jxlt+29Y7d2mMs2q0k3Pmy5cvxDoV+P7+fllQaqLannedP57nmaenJz59+iTlzLW6bb2ftv9yLboxEqpRvb2/66o6V9Mp7fVP07S89naSmeZZFiNyFotMnWh8maZXOd3zeeL8PJOC5AHBEGt1IO7lKmCdK1e+De9+2HFzb8n+R86ngwyknEZivJDixHl8CSCGYVedCwHvCpiErW2yrO3pO0u/7+rqfJT+DVb68u4PB1yXSDlLH+BiyAmCh9AZyPulECmmmWwLZHBOhLmQxHNc87+XKOOynO0IBWKxhDJwfh4Zz2em+UwuM9Ynuh1Qe1D0/YB3BmfBuY4YA/M8Yzs5qeSc6IZaNZdmIJMzxFIYBlnLSWmqz5dJYcb3BeOh5LDkrC+XMyYnrM04I+lE7yLGjtjuwO5QmKYz4RKZSuZw94gbvp674g8lum82ILnqvdBEdx3xNn9tE1N4WbBrVWnt0r0JeLu8bgLbItJ5nl9F0iDe3svlskSgqdrefkl0Y32uEOOrqPIt0S212XXLVzdfcHtfTSjb4/Pqvb90ZyvElOuYlwymRjNJ/JjGFmx5e8FQ+TZYa+g6z1/e3zPf7kkxiBBdToR5JERkpZ6C73pOzxOfP58Zx5OUFedEnC5MZ8f5uWeKFowjlcxYpJev7zyXORFTloncNULMOZJikq9iKcZjbIfregyGXCLTODOOE+OUKL1Y2KbYiioSl8sz4+VCRPNHhAAACwtJREFUymD9nstlZJ6m2oZVOq2F2DzvYE2gJMjWkDt5noyTuYlZFthsklSEMZ45BTmGc8H4IgMRjCfkSEyFOWQwFussxhtSzNJSs1isc3Testt17LqO3dDx8Piew/Eddw+PhGQw2dGVnptbS/cVzTp/KNG95roabR35rUWnRbHrMTzrGWhNdNf54XmeZeGtPk8T3Ca6a3FqojuOo1i1Vj0c/kl0kVr8RdxTIl2J7to9sV7kW7+2UmShxfC6ZNlaWyOavPwvn1NLsyQRWFurnrLM4TJFF85+D7x3vHvXpkrLde756TPz5YwxDmcNzhiM83z88MzQfebDPzLzPBHniVxTSM+njjJmKR8zhhRTfX5fT7ZU22VLW7TjIxGTEeG1Hud6SFDITOPEeJ55HhOYjoxjygZrCnPKfPw88vT5Iykl9ruDFG2kRJijDOc0IozL0kaecbUIwxdHMZZME1dDiq1K0uBsx5zkZJFTxnYyz8LZjjlnQjTMoeA7g7PSJhOS5HuNXBkOux3HuyPHfc/doef9u79ye/vA/eMjc/KYYvAF7OEPVpG2JWtXwjoCbILVmuw00W2R5+PjY12gCMvPXncYO51OTNP0atGuCW+M8VU5cBP95jwAFrFtz78W+XWv4LVVzVi7vJa238s08fT0RCmFrnVkW73m5Ut29Or95BzxvrDbG3aDw9k60cPKvtYnr2UhUNkYw/7mnt3N3WqL/PvD4Z73//Ej//O3++o2uBDjTCmJnCY+fvgHIabaCEpcCiEkYgzLCb/zbQ1jVVU5T9IDOGdyClikk9+cC+dp5vPTF4bDDxxuDrw/Hsgp8Xx65ny+cJmPlClwCVnyyK7j+PCOmCZCmGE6kZKE19b7ZrcVMaUsAzxzkqu/S55rvtlQisEUeU3TmAjWvmw3BWcLloQpUsnnrMH2Hnd3T9d5ht5zd3/Hza7n5jBw+/CO3eGIGTwDvORxv/Jh/qcR3WuBWN+/LhNui1BtDM/xeFy8wK3bWLvkvxZpePEDr7+/pDViFLGNkVIXJVrHqUV0ee0pboWW15f2ZhFLqdeP1Xvbuqu55nSo92316LYRR6neb49xztEPFuth6G1dUJDLM+8cXeeXxkTrycrKVtRj1Jo3dcBZcSbcP9yy3w+EaZJ5biWScmCOhjkEjClgHDkBTPW4SuQcq20sE4PkRuXYnUV068JTMZaULSFmQpDIVRaiwVtLNkZGyfuevr+hlMA0BTIJk2WacM7iwKBIQ/ZSshQk1IVBKYaQSFv84qU6EgymtIWy+viSKKmI+2G9vTo7Si6kEmnGZDmBFHIqxDAx28RoIiHOdDm+HNff6PD+04juW7w1AWItkk347u7uFtFtndGawLbI+a0JEa8a66zSGi1qLKWtvAplJcDNydAi0lKF8frku664a55eZ18mFC/uCFh8ui1dkkvBryLWrherZcHQda72W0g419WTTcfxeORwOLDXKcffJQZ4uD+SYyKFWMUykXIglI5pjlibKcURQsadToyX5nG/YIqpRQyhOgcyuYR6LBTc4GsRR8bGyBylcIJSfbBG0h3eOrpux27nwUSmdCGnMyVHTFtozlVwyRjEnSEd1DJ29bpLkoplay1kK5a5khC7hKmFJXlVUSctGo2hCrCMpW/bUy6IMSwyT5aSHCk4xssj3QaVln9q0b1mbf1q7oLWSrLldne7HdZaDoeDeF/r929vb18VKbSUQFuQa5a2VhXmVmOCrsW0AKVGpkvJs7wwOVdflSa3KRq+znxLKeGsZdi9jMk2sPQjvru7Y5pnwjy/VNO1VENNsDlrZBy4NVjXVY+v5/b2dpkHp3ynWIfppTxekBLa/7x5WCLSUqTZ0zjKRIvzeWQ8nyhF0mF/myamy0iYAzFMyxWX9wbnLL63dF2h8xnno+RO/UvA0fWe93+54fY+M4fE83jg/NwTwkwpmRQsIVhKlEGcpWSGfk/OMsRzno04d6bqhTeGzhugI+VMiJm+G7DW4FwiZ0fK4sntfI/ztm63kg+eM855fOeAwDD07IeB3dBJ9RuOlOSx3xr9y6lcXyo3sQQ4HA7AS3Va66HQ/MEgrSmb0LYJwmtvbfu5vu9ffLpvdOlq93IpuBqNtrTDWnSvO6G1xcDO+2WSQD8MSznwcuKogj/U++35l15RxkCNKlz1dDrnl05UrbGPpha+U1qabHUbgFq6u94si7kwzRIdxukiES0Fk4CYKCFAkuizlEJJjkwGI02lZMLLxDhOeDfgfStKmhjPY00jGA67js4dawCRiXNHjBcOg9jRKIWuHwjhQphH6fQVpTDIWYd0Rkv47lj9t5HOd+KySRPYHrCkGF7aAqQL2A5wpBherJ/pzH6342a/w3cWYxy4juPtHcNu/81/RSq6P8Pa+dDVkuK2vY0OWvf3bUK82+2WS7N1XtdaGcbZIt5/Et2Wu/0ZCxm8/kN65cywlq5vJZ6yMNi8u+v9r4spWh+Irvp7W2oDu7KjmbZ49iK6b3VjU75/3vqVWWsZdnsOByjZ8vzxEznN2BAwc8LMMyZM+LZekDMlenKx5BzpfGaeAl/yiU8fT6TkwAxM04X5cubp0weMk2Pz8fE9d8cj3js6VwjzmRQnCgZvXhwY5/OJ8/mE972kK0oiF4gxMM0Xbu7eSeAgpXWkGHk+PzHsb/DOy3YjayWn02f6YY/vehG6+iGMp48cb3bcHm+AhHEefA/9+1rK/I1/F7/iu1RTZmX9Ob3qVoZEDD/99NOymPbp06dFpNvjrbXLgtxadK/3cS1qb91+VQa8Et8lBfErXz/3vl5hWHrsys+Y5Q/3K4vu76XgemxXxAcr7oDnj584fX7iv/77A9Pli4hjTpJrpdDtnPh5S62KrFdCh5sDh92e+9tbYpyJMRDGZx7e/5XDzS33Dz9SSsQYONzuKCW/NHGiHmuGJdfbruqk+JelQs3al4Xf9l1x89ir7TJd/Ho7dbv0B64pOpCksXFvn51+O28+mUa6/yJvuR/W2/b7/eJQuLm5eVUcsY5016LbnuNa0K9vX/9/vf9/9fZb70NRnLM4JxMkSowY4/hx8jyf90zTmZTmRXRdV8jZSGmtqcUHxi2ulmG3o0uOkjqicxyPR3aHG7pe7GDS+0DGEb3F15xF9rPv99vv4hfRSPcr0NwO0zQxTdOrZjDXaYq2qPZWpPsnRSPd74ySoUzw4dOJz6czppzrVVzC2EiIhTmCta39p+P9uzsOhwO3xwdsCVIEFCL9zQ7bFl3/fOf7N9+xiu5XYJ1meKsnwS+lBRQV3e+NUhu8SFOaTPO7toq4Io25Xl2Je+9qqsFh6mMoBePskqZS0a0bVXSV3xkV3X8z1pKhccMv8uano9e3iqL8JlRwfxu6kKYoyv8JFdv/HxrpKoqibIiKrqIoyoao6CqKomyIiq6iKMqGqOgqiqJsiIquoijKhqjoKoqibIiKrqIoyoao6CqKomyIiq6iKMqGqOgqiqJsiIquoijKhqjoKoqibIiKrqIoyoao6CqKomyIiq6iKMqGqOgqiqJsiIquoijKhqjoKoqibIiKrqIoyoao6CqKomyIiq6iKMqGqOgqiqJsiIquoijKhqjoKoqibIiKrqIoyoao6CqKomyIiq6iKMqGqOgqiqJsiIquoijKhqjoKoqibIiKrqIoyoao6CqKomyIiq6iKMqGqOgqiqJsiIquoijKhvhf+b7Z5FUoyvbosa38LmikqyiKsiEquoqiKBuioqsoirIhKrqKoigboqKrKIqyISq6iqIoG/K/dSzMFFQPJucAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "def show_subplot(original, reconstructed):\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(original.squeeze() + 0.5)\n",
        "    plt.title(\"Original\")\n",
        "    plt.axis(\"off\")\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(reconstructed.squeeze() + 0.5)\n",
        "    plt.title(\"Reconstructed\")\n",
        "    plt.axis(\"off\")\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "trained_vqvae_model = vqvae_trainer.vqvae\n",
        "idx = np.random.choice(len(X), 3)\n",
        "test_images = X[idx]\n",
        "reconstructions_test = trained_vqvae_model.predict(test_images)\n",
        "\n",
        "for test_image, reconstructed_image in zip(test_images, reconstructions_test):\n",
        "    show_subplot(test_image, reconstructed_image)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Copy of vq_vae",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
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    "accelerator": "GPU",
    "gpuClass": "standard"
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  "nbformat_minor": 0
}