autoencoder/VAE_Experiment_with_Face_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "VAE Experiment with Face Data",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manishiitg/ML_Experiments/blob/master/autoencoder/VAE_Experiment_with_Face_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b657Qn4cRFnS",
        "colab_type": "text"
      },
      "source": [
        "# VAE Autoencoder Feed Forward Network\n",
        "\n",
        "VAE Auto encoder on celeba data set \n",
        "\n",
        "Here is the pretained model : https://drive.google.com/open?id=1jG5zFI5_7NFKgNj8xTXRl9XikptI0iRX"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j71rTkGIDjqd",
        "colab_type": "code",
        "outputId": "4e5bd882-e97b-4fb2-c242-7be49560f6ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Let's make sure the kaggle.json file is present.\n",
        "!ls -lha kaggle.json\n",
        "# Next, install the Kaggle API client.\n",
        "!pip install -q kaggle\n",
        "# The Kaggle API client expects this file to be in ~/.kaggle,\n",
        "# so move it there.\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# This permissions change avoids a warning on Kaggle tool startup.\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 66 Nov 12 07:52 kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qu748iQZF5x",
        "colab_type": "code",
        "outputId": "490c2273-c338-4e44-91b2-5d0fcf8b69a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# !kaggle datasets download -d ashishpatel26/indian-movie-face-database-imfdb\n",
        "datasetname = \"celeba-dataset\"\n",
        "!kaggle datasets download -d jessicali9530/celeba-dataset"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "celeba-dataset.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyuQIpkVa0kP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile\n",
        "\n",
        "with zipfile.ZipFile(\"./\" + datasetname  + \".zip\",\"r\") as zip_ref:\n",
        "  zip_ref.extractall(datasetname)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKUgaG_EX9mg",
        "colab_type": "text"
      },
      "source": [
        "Above, download data set from kaggle. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAUd0NPmbwK6",
        "colab_type": "code",
        "outputId": "a6494398-e0ae-4c90-b13c-6c91e75f51a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import os\n",
        "root = './' + datasetname + '/img_align_celeba/img_align_celeba'\n",
        "img_list = os.listdir(root)\n",
        "print(img_list[:10])\n",
        "print(len(img_list))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['178241.jpg', '197806.jpg', '145006.jpg', '063694.jpg', '137139.jpg', '118467.jpg', '122762.jpg', '126971.jpg', '108310.jpg', '015369.jpg']\n",
            "202599\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaXMal_6YCz-",
        "colab_type": "text"
      },
      "source": [
        "Just to see if images exists "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjsbslQAcSe5",
        "colab_type": "code",
        "outputId": "1b749b90-52e1-4c97-8c8e-566f42f3da33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision  \n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import datasets\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline  \n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rl3pWfVwVSd-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import PIL.Image as Image\n",
        "\n",
        "\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.ToPILImage(),\n",
        "     transforms.Resize(size=(64, 64), interpolation=Image.BICUBIC),\n",
        "     transforms.ToTensor(),\n",
        "     transforms.Normalize(mean=[0.5] * 3, std=[0.5] * 3)])\n",
        "\n",
        "batch_size = 256\n",
        "celeba_data = datasets.ImageFolder('./' + datasetname, transform=transform)\n",
        "dataset_len = 100000\n",
        "data_loader = DataLoader(celeba_data,batch_size=batch_size, sampler=torch.utils.data.SubsetRandomSampler(list(range(dataset_len))))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxN_CM5KcKdJ",
        "colab_type": "code",
        "outputId": "cb87f244-9491-4747-d102-dee728af89d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "def show(img,renorm=False,nrow=8,interpolation='bicubic'):\n",
        "  if renorm:\n",
        "    img = img/2 + 0.5\n",
        "  img_grid = torchvision.utils.make_grid(img,nrow=nrow).numpy()\n",
        "  plt.figure()\n",
        "  plt.imshow(np.transpose(img_grid, (1,2,0)), interpolation=interpolation)\n",
        "  plt.axis('off')\n",
        "  plt.show()\n",
        "\n",
        "batch, _ = next(iter(data_loader))\n",
        "show(batch[0:64], renorm = True, nrow=8)\n",
        "show(batch[64:128], renorm = True, nrow=8)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c4SVCQLYoefT0c/aP4PabV4nCdZSKeCYCXEBxZx1aaaIwwFYVrSjgynofZz2HwyG5tRhb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cGhptyKuawAOpO7ZX3xL2p3gH79G2luVqzX/4+c/58quv3yxMh5bZlbJVxXA4JAxD8jwnTVOa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V\nLymNYVpoVtsGv2nIS8VgUqCtR6MLwGNU+Hz08IBbN47Y2djA1hWBiBH4NFWDEgWgGQ9PiaOI1Xaf\nP//Vpzw6OGY0HjOdTiiyOaqu3crIkyx3Eq5td1hbT8hsiacNtXbk8jCOMDhTorKoCITEkwGqrMny\nEqPh0UnO82GF0gZfOHJz/ZK6xQJcc54+Lz75BTSwtoKpbsi0wQiPOs8YH5w6M6ikpqndjjK8sk7Y\n6lKRM9cGEUV0oohaGxxI4vwVxMWc4Hg6589/8Rn3Vw/4T//sG3Q6IUVVwXiMtq6HyEdzZFaTeDFG\nBlApxkcHfPngCfsnJ/TiFM/3LnozKeWCYaRQSiOkIIwj+ksJqx23yiJOCAOPOAlJOy1kEjEcjDl5\nfkIoBGESYnNFPquo65Le1irCixlM55yMM7KsQFqDLyTKmlcnqheJ5KLWDaeTnJVuTOA7x+y8rJmV\nFYEUxNLDQ1CphsPhGATUylKUjpP6Kg9UwLkh79dcpb9jIGQYHZ1w96t7/Plf/SXfeftttv/kTxDW\nwzQKpRvCMGR9dRVPRAR+gK5rMA2B79PuLrNz9Srr29tOOeC34qUv0QLy9kKa5PwPcQyNg9MJH9/f\nJ4lDluMEdEmVF9CA0IpuO6K2hsl4wsHpiP3RlFFZYg1U1lJag16AqH0rSeMYrGC91cELBcpoxkXF\nXAumMuZX+1Muf/6EP/MDNnrLrjLwfWSrhfJKpqNjjLBsX7tOVXZ5/uyXPLnvBiKVrtBN4zwzhSBN\nQm7f3OTqVotet0UcxajREIFgnNUkoVqsDSSdTkoYBKRxgEebou5zf5Dx6dMxx+MagXEaRmIBlv+t\nIz1PzhetQRgEdPt9Zo3l8eEx25fWiOOAyzsbBH6EH0YYa6lNjRf7ZHnFyWjKg6MhcX8FfIFB/vbQ\nRLiJqpAeNZA1mqAVUVaaybMBaRqTjmd4gc84NwgjudxLaSdtRLvL3aNjPr3ziLyqWW47ETi7QLxL\n4Sh5YvE9jCKfzY1lrm+v0kl9Mp1wMHCSMQKDMppZWaOkpLO2TOp7JO0E6QnitEMvXSbopgznOXtH\nJ4CgnYYo05CVAq1+E6T+G1eaEA6EIjyQbnhdMksAAA7HSURBVDquDDjfY0ltDFWjsVg8YWmQGOsq\nQimai5Ld9dIvn+PvCUKwumF2coqtKi71+2yvrRFHEY3WlHVNoy1hGBH2l5EItDbUtSCKAkTQsLSy\nwu6t23SW+i8hI17FSb7cJL9CFXM/uCjT5qXi84f7tAPLW9cuc2XdZz4fUjdtfKHQWnE6nqDmTo17\n2jSMjMFawfkz8VxiP6s1n53MnAQlPs08xzQ1ShmUtqgwZVhFfHlUc+XhU6I3A1aW2wShT2k0s+kY\nbT02dm8h0yXOvjxhnuUOTud7RDpwvNNaLXwvE65e3eL61Sv0VndIky7DOz8lKodYaRlM50S+hx8E\ntJKIaLF/9QNJjeTz/QmPjidUSjupFPECuME57uDiv+6kzfl5YqmKnA8//AQjAm5uLLOztsLGqttv\nRp02fhhjjKWaz5jkBVmu+PLxET/+5DGVeUiv1+H6tR2ubi69dLu4VVcchcwrjUAzKkqOsjn99jJH\nz86YliVBGOCFIR/uDchry3/+l9/h27fajCcj/ubjL/jFFw9RVjJRhjgvQHpEUYKUObVu3N8jPXxf\n0E4T1je3uLSzgwqWmH74OfPjwYU/DkZT1+5iWFtborPco9uKiGNBoyoG4yF//+UD9g9PWFnukRYe\nRVW7MrUxr1DTfhMoID2P9ZV1Lq1vUWYnlOUEY40j3S+EvYzndHaV1lTakCRdrl3b5OTshMPDAxr7\nwjbR/q5r83cmZ2MwlWaju8QPv/seV6/tEgUhFWCqkrpRrlH3fTwpQWjiOCROQpqyJul06K2uIaXj\nXXqca/a8TDR90Si/LF3yIlEFnucDBokg0BXHJ6f4gc+VVptQJGSq4nQwIJtPWW2FWASqsYse9mVe\n6KKn0Q3Ps4okCLBW0WiNNQ2NFQQyJpAx0sLlK9eQ4YxHjx8zndUkcYu6muHHkrWtyyTL65yZNp/f\n/xWz2RzfD/CsI5RHQUgTNkijubq7w/sf/Gve+eZ7hK0+e199TOlZlpcci6KqDHlWUhQlRV2R5yVJ\nVOIlAfN0nbGcgjfA9xTGWCfFKASB7+GHTi1PqfoiSV/07e6M6wb2jzNs2GW1I3h2NMQ0lnnRJi0r\n4iimaSzTrKBSNfO85v7+gOeHIyptODoeM57MEe9coxUHiI7AWEuapPieRWUlAsPJbM5ffHIH8c1b\nrHc65GXN3sEpc215NpwxLho+frBP4En2Tib86JM7PD4eEMUxeV1yMhmx3OuzdeUGl3ZvU1Q5whrO\nzqY83XtGHIWkyyuka5s0NmRzc4UnZ2fUSjGZzEE1+FIQhR7TsaQTC2IvYTwt2Ts44LNff8W9J8/Q\nVtJOfc4JKb7nKGCNsa889M4ffEkcs7N9mZ1LW7xx+1scHh/z+NFXjCYn5FpT5noxVFooIvoRu1e3\nefvtd3nj5pv8zx//JSeDE4yq/+8NhKx1/hdhEBNFEoxTInBOwB7WaoeY0CWNqp2BbVGQ5xnD2YRn\nWcHGrTdodbtOdcCKi6eGEOcp+aKkPRd7vnj9RSMdhjGe9Ihjn+3VDqu9lNPxiKd4LC8VeFYjdcFm\nJyCNImrVXAyXfpO4fc7wkI1C+k41odZOLiMME0I/xZfQTQNu3brBpneKGT7DqgLrS9rtmPZyj3hp\nmaZ9iXv3Dvj0iy9pdOOMjl7CT3qeR7vd4b0P/pTv//A/sNrv8ujLD5nc+TGpmbLUbtPttBEioChq\nJrOM2Txnms2Zewk3vvFD3tt+k9XvDnjrZz/jl7/8JYPBKWmS0G63WFlfZ3Nziy8+/4xPPv7YMTsW\nx2qFxRjt+h4VowiRTUBtPGfhKJ3FRp0VVHmBFQ6720li8rzmdDyn1g1YSaPh+GTGzz/6iv2ne6wu\nJwyODx1xWWqyIsfzJL4wfLZ3xNkk4/s3r/DW9jpvd1qc5Tm9botJZSjKmi+fnPDpwwMeHJxS65q6\nUFgTkeVTJtkMwoCd7V2W+j2klCh7jHdwgDGK0WBAECUIETE7G9FojS8NvXbIer9NrxOTBh5hIBB6\nzmQy44sHz/j41/d5fnSIlZIwismK0snVCFfpeRo4NxV6qdKUQnBjd5cf/uAHeBi6oebm+9/mvfff\nZ2//GY8fPeb4dMB8PmapE7G92Wfj0jbv/tG36QuPgwcPmezvgzn3AHq5sxW//0CobhSn0wn4PsaD\nO/tPWV9dZ3NtncAPHF0Rhx0cj8dMJmfuCW4tg9NTvnjyMYUx/Pt/+++4ce0arXZ7MdV1b+ziFr14\nq45RcYE59J0erdNydZw/I31uXl5jtxEUVUMnMrSTBN0yWNMwyyuMtSRRTFY7bKYTlX41ORtVUVon\nMiWkTxw7jKZD2DT0um02Njdol5rQTEmjmLidIpMYubQOy9cYqYCf/vxDDg4OF38PbqpozgEBliiK\nuHHzTS5d2kLogmZyQNtOaPdSet0uaZIghaAd+7RjyTyVzDJBFqVEvU0uXX+H6++0+P4H/5J79+5x\ncHhIEEa02yndbofD5895eO/uxfkZY6iqglKVNNZD6xBtAwhiN1zDp9PpsrnaJQp8wshHNU5wu9Vy\nKgcHJ2Om8wqEU+l31FTJ2bjm5OSYpjxCZVMEFXHqoRqNbkAGPlZY9oZjRp/lHI1n/PCd67z75lWW\n2m0qG/HTO/v83ZeP+OTBY3LlxLR1rVGeJPCccPjJ4BSsTxQG+H7AcDxF1Q1FXnN6fEyZF1SlZDYo\n6ZqUjh9xabXDGze26C91iXyJUTVa18yKklleM5qXLK+usLTcZzyd8/TpYzwafM8nCgyBVNRCYnhx\nOUgp6bba/NH1a3z7G2/QX1tmcnjE2ckRN958g7dvfofD21ucDiZY6bF1ZQOpM3dmheb+558xOhux\n1d/g1uWKg+GALM+dcJlwvfTvPxDCUoqGB4ND5nXDJM+p7zxgtddmc2WNpW6POImx1lAWOdq4yVxd\n1cxmc07PTvnv/+MvGI2H/Js/+zO+8cYb9Pt9Wq02QRDiAGcLdItwjHrff6EZI6XE991btNaC59HI\nAD+MWel0MNoghQNJVzRoXVPWU6Ioot1uk2nwVEVVOR+Uc3l9FuWue7+CMEgIg3gB63IDieUVN2Vu\njafIuk2QRogkRS5fwlu/TZOucOeXH/HRh59QljUsuJgX4mOLykAKQRKHJFGAlwTc/KMf4Ksx9uwe\nnVZMGCWYpsaogsDTxL5BR5K6GnHy2V/RCgK23/oeS/0V3v3u+7xVFlS1M8IZD8/4b//1v/Dpp3+P\nVgojBOPJKfP5mAbwwiX8sIUXpQjhITHEcewGRK2EVjtBeL5jkliI44i6VozmObNaI7wQRIDFCSkL\nGSK8LtpMKcpTQt9VUVIIat0QRxJPuJJ3UtT84uEzzuY5/yqreO/mVXKVcffpc+4dHTHTOV4gaBpv\nwdYxGOkUDcy5qXGp8Xx1MbGdZxXtrkENJ8gKdpfW6douqd8ikW3a7Q79XhdJQ11alBLoBbLVSMH6\n9g5lNmU8mVJUmtgX9DspuimIpUchnOOAlJKlToedS9d4951v8eb2JU73Dzndf8ru7ja717eYHN1j\n1ChqZeh7sLa1y9bVNc72RszPnnN8PGKSDbn63W/zwfV3GE1mfHn3Lnd//TkPH97hcHjGvMzRqN8v\nOUXgs7S5xmA8YXwwIG2vYGzI/adP+fzuXeIwop228H2PwJds9HskgU82zzibTKlVQ5FP+Onf/RxV\n1xwdPGNleYWNzS2Wl1cdg96THBwcUhSl04s5X6Xg5EvOLQCFgMoPGDcex9OKQjlZEa3VRfmb5xlH\np1MKvSiRG5e8YRCBFZi6XjTiAou8WHBLITFaYdFYa/F853FycnpKlc2w05pYtvCTZTy7AuOa+eEe\nf/OTv+Xp033qSoFn3HhcaxqtXjxgsAyHpzx7/gzf89BNiLf5bUZZQZWdEFYVwjbURUU2my/8Qhuy\noqQef8l4MmcwnrN2/ZsE4UIBQjdorfjVL3/OX//oL5nPZu7BYAxZNsMPYsK060ygPM8NwlSJ54FH\nw3A05nkAS6qLkJ6zfrcW4WfM5hlf3LvPdDqmsQmIhkZXCzids6vzww4yWDxUdOO0ZY1AeiFS2IUE\nCIzLik/3jzmczPnF3X1K1bA3nmE8zykb+jFC+kwXXFdVK2zgUZYlWZYhBHhBiKprVGOYZTnm8JRe\nq8XVjWU2NxL80qeZGvIzxcHRkAaDtIqqyB1+uNKcjoY0RjPPc/b2njKb5YRxC19CqSyTrGJe1g4A\ngmUlbfGD73yPb33nA27fuoH0YO/xPY727vNs7z4r622sralqQ1YoVJXj3b/H7t5DdtfaBGbOSstg\n1ltE7YClzS5r13dYv7rLm994m/uff87dx/f4+MtPebr/+B/Pv6/T31zqde2169cw1nIyOMXiEYUx\nRZGRZVOahRUCOJZ4r9UiDHyUcm6+WVnSGCeD32m3WV1ZJgxDwigiiZ0dgpCSoii4f+8BRe6gZK+M\nic7XQgg8X7LRb7Paihx9Z1ESn/8NqnZenXNlOZ0WlOoFR+/cOsIuJD4vsI6At0COuJd02kYbm5ts\nb20SNiW2LvHiBC/ugBe4oZJSPHn8hNPTswVIZ9FP2HMV78WUTwpu3rzJ5samew3r7BWqbIQtJ8hF\nGWUXDlTgBmNKawcIFz5Rd420v47nL2wtrDOXOjo44NGjB9SLh855QSa9wKm0ez5CBO4WPyet91qs\nd2PSOCSMAiziQnVcCEFV1+wdDhhm2omAIS5UGM6VEq3V6HoOpsL33GegG+tEyISkVvXFWZz/Xu9c\nyX9BSFaNwvMCpPSoqtKhmhZtju8HhGG02Hc6lE+eLVTopSSOQjppTDuNkcZDFxY/8Ik6PlESIoTF\nNI6+qBvD4fGAeem+W2VRgJCEYYg1xgFtKpf855nQSRKubF+l1V8hSUOEFFRFTpnPyacjhHWSONo4\n02OtnGlut9Om3+sgjFpMzD1smJJ2lwgW6ypVN8znBWVZcjI45HRwjNb6H+w8vzY5X8freB3//+J/\nQ0r7dbyO1/H/Ml4n5+t4HX+g8To5X8fr+AON18n5Ol7HH2i8Ts7X8Tr+QON1cr6O1/EHGv8LMcNS\nfyw7JLsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U0wRR2KlcJDR1Q50LVpkg0ZJ7WyHDyHO0yOj1R9zeHbOdxNzanrBs2q7MpAPK\nbE0ZCrK8orWexgkaC1GkMU1N0zT04wjrHFlhqDeuUyrJrTv7/OEfvcVk3Kc6mVNlGcvVmrw27CUD\nvIqZVYbzPKcwgu2TU+68/hplWfD86IQXZxf04jlKOKbTAWGccr7IePbkOftvztiaHHDvjT2y9gwl\ny87ghpokDllenNGUZaevFoB1zJYFQmmm4wlVUVBkS7yt6SURZWM5m13gp0NeO9zi1s6I79ze55tv\n3KFpawppwJpOLeUciC79sM5soqsYKSRmvWTSS4jjhMwKwnSbtoXV5aorp0UpYX8LwgWjNGWSV9im\n65wSYYgxBmc6MY3d1PZ/L3BqpRgOBuxNU2azJc+eZWghGGvHaGfCnRs3ufXd7+OihDgOmYzHmLrk\n+mv3UUJgPBzuT1EI6tWC/3J+wcUyJ0gi9nd2GVrDqnFcXFSbdi7LejHj4eNn3B+E/Ptv3+f4YkkS\nKMZhwDvX97i9u03rPBpJKD2hFIyTiO/e2kV7R10U/NNH8MOjBUoqmrZiuVhg2vZKz/gbZJDq9LjC\nQag0sdb0pWd/a8jNnW2EDuhFAQqPqVtCKXjtYJebO2OEaWmbirN+j1B1csMk1ARaUjebwruArkbo\nrqSJF/malYMwGiCdRGpFL0zYHe/w9fvv8MZrb/HTX/4K17SAQ2pJoAT6VXeFFAShIkqCTchuMHRK\nGC0ltycR7xzEnK8rTpYVqYEwDNiebvHa9pR4MEQmEUkcI5saVxu0t9za3eJgMqGpK47PT/ipeYB3\nljQO8Q5CXbyS3COF4OBgwte+dpOBFdTeYbygFBHJ9jW+9gd/xHDngNcuZlzOlwgl2dmasH9tn7Sf\n8ictLNZ5J5q/ecDWsMfZ+TmXWUl0csIbl5cEo30G4x7pIEKUVXe9hKRYLTl9/gThLEpCWxfc2j7k\nzo3bxP0hg8EQ15b4uiGSkjvXD5mMJ+T5mjQALRrWq4w//9br3H/jHu9+9Al10QkkOr2zJElSoiQl\nz9YorTol1SuL7uHO9Wu8M9ghjHvkxYrZ6SUSiNOYP/rmW7x5Y5snHyWsf/Zz8kUBMkCpgNZ58qpC\nKtURW1/iPb+8n1PLrnShBVWeoZQgiWNCGgIpGfWH7Ozs4sIYvO2Uc5tukrKqODi4xsH2NouTE37x\n5BEX5xcduRBGjMcjgkBytljy/DLHtC1NVTBblvzq48ewN+AH37iNEh7hPNp5BkHA/nQb5wVVXmBc\ngw402wND5NaMw4jMNfS0pGpaStORPYM4JpQK2xpK90pKtwGnEFcdLMKZTgHUNAyjhPH2LtZZ8tWS\npm46NrlpGKQJ/a0dbFnx4nu9NQIAACAASURBVPkzfNMSa4ES0ItCoiAgo/p0IYVEyAApu2L8vKr5\n4OyUrwtJ4hyiqtAIpsMh4/v3me7vkf3D3xOIlkSDFJ3l9QIawHpPYDxN7aBpcY2jqR114xlGmu/d\nGPPaNODpLGPZGNZmTS+OkQj6YcT+3jWCyYhqvSArMrxUiNYQpQnbO9u4sqTJMhIZEEpNIAW1adEb\nWe+rnD2JAsZpilxXWOdoEZQIgihiMN3i8PYd9m7cYnFxSbZcsLU9ZTgcoqWC2x4ZhAxHQ9IkoakL\nWhHQesGyyFmu5lyLNNcPD3nx8gXzlzmvdL8nL15yfvqSQEukUJi6YJjGfO3ebfrDKcv1itn5Gm08\nYOj1U3Zv3kJqgSkWHL98Sj+JuXbtGmEYka3XeBd0DLRQyI2eNs9zqroksN1rpjU4D8ezJaGUXJsO\nsEqTzUuauiM7Q+XZOrzGZBij6yUPHn3IaV4gUKxbi1SbsFlKgn9GZvClr8aBIg0hWy0JBLzzxgG7\n+/ucPj+iyCouz08JfvpP1M4zOTggjBPOTo752U9+zGKx4Otvvsn2cMTLp0/44IMPuFwsSeKI0aBH\n2RiKoqHICqwD4T2mrrl89oSPnz7l9CwhSWN2Rwk9rWmMRTqHqiuc0FhraKzDCof3krwooRKsVjmX\nRUPZWmrhCNOYUCuCQFHXMa01NOYzuNn8OmvAtVRNS9XaLqTLMlpjKLMMa0ErTVs15IslwoGvWxar\nNUVZ0os1vUhjTUAahyzWXXOu1ppbt+/z+ut36fcHRFFMHMfcvnObYGubVli8LVHCYqqcpmrI8h6L\n5QJnHVoItBIoLTFCkZWO3FoENcimI4mcx1lP2zhen6S8udtDKwMbSeO6NggR4mzXbO6Fx7YVTV3Q\ntA3omMY6fJkTJgnSu47BjjRRqNESVk0DuK45eiPDE86jrAfbdcUYoSi9Z73OOXn+AlcYVnnG+VmX\ng62nuxw/fUZjW9bZirqp2NqaMByOQSgWqwW9fkooahpT0hsGvL57yDy7wS/mx4CgyNY8/vB9FosF\nWgrYiNWddbRtS1FmrJdzyqyiDBOkb5EiI9AhWitM1eIM6DBCRgFPnh9xfn6OGu1sNkRngYwxhEFI\nGMbUdYW1hjhOUbLPy1XGssgQyqJDzSCNkEknD9SRRogWJVt0AChFGigkgqxuWeVrWusJXMDv6rn+\nBeCUOFxdUawyAgWp6qR1q6zB1fDk5UvWZUU8GKPTFJ3lPH7vfT5691eUraXMCvZ3tsiXC16cnVMY\nxzCNWVwuOc4zkgAy41jnhkHPkq9XvHzyAFfnPJgv+Y//6PnB/Zt85+41GmmpyopFVdDKTvsoEBTr\ngvnljFWWUwaKs6xm1jiEVsRKkcQhzhmM3bTuvNJ3ilc9m5u2KRxSOFrTUlhH3ZTMLgxFXnXtaCqk\nxWJbzyxbsFpmxHHAoszJm5YkDOiFAU3b0tuEm8Y6ev0B/+P/9L/wV3/1V8RxQhCEJL2UXhpTzo6Z\nv3xAP7AUs0uE9IS9iOPTI8qqwPuul8t7R6gE+ztjkjAhK0vWdUNWN6wrQ14ZGmFxssufF1lDFXp0\nGJGGNY2D1hqyqiJrK9arGe1FjXAGJSRt7RFG0VQll9UL4iSlqKqrFk+t9EY+IdBaYjZ6BI9A+E2H\njAO0pqwrTo6PePjgAbOjIy7XM8raMJkckC8KlqsZIo6I+j3mywt42LC3s8NoPGU+P2cyGjGIBTpK\nUYEi7Uf0+xFKCaw1nJyd8PCjD6nLkihNEBJ6vYTCWJZlji8KVrMlykryrDOabdUwsxfoKEBLMErz\ncrFmcXLO5fkFq6olTQymaQCFMS2tMaggJNIaqTR1U9Hvj0B4CgwvFgvuVxmqrlFFRShDpADfGGy2\nwklHUdcUrQMUUkAINGXJvKzppT3COEL9vsJ3iUOYBi3AK82zlzOsn5M1oIOE87Lhztt77Oxdx9SO\nuBdy/fY9vrbIePDwEWXRMNgaoQJJdHrOwFqchGy9xpqGvDQsG48xEts2rC7PyZYzepHCOyiKnBcX\nM+4dbDGZ9JEipG4see1wUjAcjhil0DrPbLGiEp6ZhVII4kgRRAm9JMZYR2MMRVXRWnvF1nZNubJr\neJYgsazyFc9nC+7dOGSgFcWyQAUxwaQraIu6JV/M6Q0T6khxtFhztspIAk+oIFDQi0OiMKBqLFIq\nRpMtRltThFAkUcxwNEILy4cffcQn7/+Y7377bZTUDPf3aJqGT54/39xpQiI6gR69JOTWbp/b233S\nYIzTmsoILlcNLy9WPLlY8cnpivOi5sPTNfujAKk0ozgh0AaEp7QNedsgY43Jc5p5zni6Szyc0BZr\nlssMOQiQaUThHRZobNeh0mlZBeM0oI09ZesRquuN7VrCug4ZU+acXZ7y8PljvnHnHv2tLUIj8bUm\nTAZsj8a0XrBaZYigT2+kIQpYlzmmLtjbntIfpPSGE9h0swgpcNZQrJc8fvAxJyenDCNJqLpyWy+J\nOVoWLGrDJE5oGxhOJox3tlHeUhcZ1njiUQfy7MzxT79+yuPjcyZpjGk8om6oixwf9ZBSEcUpQmrK\nqgY8UZQiZJffr6znp0+OeP3mAdsqpjya0SdBSoFLPUEvxAXw9OyC83XVRRROoeKUUb/HeZZT1TVK\nB5/fqPDfAk4hJVJJpOpUOeusZlXUXNae2jY49Zx0a8y/v32Xa/t7uMqihOKt268zCmOa0KGVY75e\nQChRgWSZlazzhnXVsKwalJIEWuFti7EtQkq8lMRx13SclxWz5ZJxFDCKFEkSEichrnKIdYmQEBQV\nIY659Tw4XzIvCuJQMhik6DjicrZkXRRUm5zh6vy6chOBlARK4q1htVry7kcPuLU94a1bNxlPR9ja\nk+gIFYVQV7jpFiLRfHz8nB99+AiE5fpWivIOYR1JoEmjiHXRXVjrPK2xSOERiURoSVsWPH/8kF/9\n7KccHh7y+v23kaaifvGIIluSVwVWeuJEE8WK3iClaCtO5g29UCIjTS9OuL0Tc3d/l7fWE/7uvZf8\n6uEF51mLFo7MdAZonISEUYARgocnp0x2JmxP+rSiK7IHorvHUm+UwCjmdLHg/WdHnCwzjLWs8rxj\nd70n1o6dYcC86jpdnLVdcCYlQniEqXFNxXuffMQgDHnr7a9x4+49IpFSZzWVaWitYzgaEQw1ZbNk\ncXrKxelLXNuyvTMl6aWkabrBvcM6izEti8tzjo+eUxQVw3iI1oo0UvR6Ke89PSFKP+Yvv/sNpuMB\nofXo1qKcQ6mQaJoi45DFfMHj5y959OKUZV5hjSPWAu3B1QVedy1pSnfNBHVdo5QkigI8kGVr6rri\nh+/nvLk/4d98/W1Gdw8ZhL1OmicaDC0PP3nC3/74XU7WJUmc0KDxSrO/u8dFXlA2XWno9yaEssJw\numi5vjPk5uEuz45mPDxZclm1aKW4WEv+5oc/g94W//N//5f0eim6aRjuTaEvOT5/zuPHjzg6veBi\nVXK+Nswqzbr1FBZq7xFNizaO2DmaqsSZFhCdZM0Ypv2YULRkeUZbKoaDlP4gJQglNIY2z8GVuEjz\nwbMLjuYrpHekgSIKFVXbUNUVVVVvGrM/7bvs9Kdy03gswXmUt5RFzmq94vTynL3JDv3BkDhNkWEI\n2tKGlk+Oj/jbn/yCXz97xtfuXUdIj7cGbx1aePqRYvaZW2IopQmDgCiOkFLS2JbVcsHJi+d8+Mtf\ncG1nn2Ea0tYlTVMhlCAdJERhiA4gTkLq1pCpmEXdUi9yxmnDzrC7nUlrG+JAEEcarUO88VRFQ+E8\nw0GEoGMJP355At7xzuu3OdzbJ0hThJLoMEZUDU9Oz/jh+w/50YdPuFxnmM90ozTOI60F44hkgPIO\nU9dIYwniGCWhFwYMkojZasaPf/0zlLL0J2PGt6eoNMRnBco7wjSgMSUvnx7z8ONfU5Y516/fIB0O\nSXoJcT8lUBohFEooBFAWOXm2pt10ITlkJ27XcLlc8Tc/epc0jvm333yLfpR2+XIUoANJKwzzxZLF\n5QLrDBJPIDwKSyA0Costc2w0QghJEEQgfHf3hTjGGEdV1VR1S9N4liZnuVpiXI0cDlHDBCkkTVZz\nfnrMf/3Jz/n5g6dsjbfQTtAYT2EcWgUM0z6ty0Gqjh7+fcBZty1Jr8ftW9cRNFSmoWgdAsmoF5HE\nEb8+Ouf8//pPpP0x/+4P/4hwmCB8QpmdcnR6yrOLFZcmoO4NSPoBUyfot4aizMnXa1bLBUW+pmla\nsnkne2taQ9sanO/anCY9zfY4ZrGqeHF6QTgvmE7GTEYDVCApy5wPTtd8+PICISyJphM2VCXGiU5k\nbC2b2+58ZnR3Aej6oT0SiLRCaUk/iQiCgGW2RsuAKOnhkTS25uT8jA8fPeLp8TEeTxJq2tbQtg3G\nNOAccSCJAtnVUZUiiiLSJCGOY5QXONM1OtdVxcMP3uXO/g6HBwccPe/u9qCjmNFAYqwlDEBLgXOC\n2gqyqkFHknQ4QQUpy2WGjhO8qtjphdyZpqTCs2w9bVFTWQ91S1sbaiE4T2I+fnJEURiuX79J0hfk\nWcHjpy/48Mlz3nv8nJPFitY4jPcYx+YOdIIkFLStoTQC31hMXuCqtpPLhRGT8ZCdyQhrG5b5kg8e\n/BqkpDGG/cO79EZ9mqZhtb7g6Scf8ujj91mul+zs7TEYDdBhjA4CdBR2d+CTEqUVURQSKUEoHFJC\nay1147BNjVcGhePhyQX/8e9+jJaaP/vON4j6CU5rsjJjtVpinWc63eJavmSnH6EF9OMQ19aYOqfJ\nltDbxlpHnmfdDea8p6wa6rqiqlqsVWgpub43ZKeXkASCUFfglzgvMO2S58dH/PTBc87zmqTnMNTU\nTlC5ENt48qZLFdD2y26E8OXgDJQgEIb1aknjLIuipJ9GxM4z6iWUjWOe1Zwun/G//fX/yYtnj9lN\nQlxbMstyXq5KFoxo4hAjVFd0tQ7lQUiFCiLSwQjrAakwzrMsDUVjsc5jnWO2KpjnNTevdeHsxeWK\nl2cXPDk+wViYFy2fXCx4NlsBlhCHkwqU7MotdCWUrrz56jaKn9bq5KY24Dc3S3rVj6qkYDoZ0zb/\nL2nv8WtZkt/5fSLi+HOufSbzpS2TWW2qqptdZDuKImYkaEABghbaSIuBQC200Gz1lwgQMNBCgABB\n4ErQVtoMoNFQA5Iz7GabsllVaZ+9/t5j40SEFnFfZlaxu9hqXiDx8hlce35hfvH9fr4923pD3TX0\nBq4WM+arGdb6c9NBkqCw1I32gmZncftuZxJ5vbBSijAIUUGIuKYYhAHFeEgURswuZ3z+6SOWVzM+\ne/QZm92OQFuOiiHZZELdb6l2c5wQlLpHO8l4MOHw6AamapFRwPDgmHzlOBnU3JumzLclTa/pe8u6\n7ChxJEGAcpaqbhgXGXW146OPPwEpqZsGKRwnhxOSZy+wZl+YxuGEwDpBpgSjWNBoT0/AOYzpMFrv\nlVYRWZ4xHg3o+o71WqBNz+nZE3b/Zst4ckwYpYClaUu2myVtWzE5OGR6eIyKAmyvCYKMIM2wUqBt\nR280SRwxHSTcHCZgEsIoxJiOtuyIjUJZizOOT5+e83/8q79iu97x7sM3OByPSKKQwaDgxmREKAyb\nZc6dwyFSbAiVoHaCvm+p1wuC4QlN23F2sQER+AFc1hjdIWXMoBhybxLzwduHTEYDwiglyYeEcYi1\njqZf8Ysvr/jkckdjoe4dVa/3zqEeqWLCMMWYDfVmhe663684hbPU2xXzK4uNEmarBt1DngQEUnF2\nuaRtOxCCJ8/P+CvlePMgwzjBXIfoaICTEWbPIjJG0+ueXblls93Qti3OGZACISR15539kzylbTsW\nu4bT+Y7/628fo6KY994+4egwx9maUO14fL7lbx5dcFZ2pElAGkiwvq1vpcAKidH+Q/NsL0mSJDha\net17Kt5e8RKEIQhB1xsSaxDOECpJNijQvcZZqOuGPA2QasTji0vPpklC2rYlDAKKQc6qLOlbz98J\nlMTuoVNBGOw5robOdBgJt956m7vf+g6XT5/z6NkL3LNniCDg5N59hnXF8OZNbJLy6ItPkLoiCSPq\nukUJxcHBCaPJEVfNC/LRkGw0RMVL6rZlUSo2BqxwBNKhjWGrDWvbkSjFk7MVD2+X/PB73yKMY3Tb\nobuOIICnZzN0500OOEccStJI0fZgMFQdWOPFAMKBkgEyEjS6JggUYRQSxxHDYoTtHdIZgiTG4Vgs\nLkBAGIWe+RMGDLJDBoMJUijK9YZgPCBJUrLBCBEEGFth6QFLFiluTQdgOlwQEwuNMA3lzoHRSOeV\nN7PtlnVZIYOI8cEB42HBMM9IlKRvS8ZjL+7fbnfoviOQAicCdps18uocaxxCFUgV+k5/3+FkRJqO\nOR4O+MM3j/jOmydMJmPCJEXGOYQhttNcbRo+OVuwanu6vqc1BmMFoYIiVgShZXg8IQ3h2eUl22/g\nWX1jcRrnWFcd1m4p+x3Pr0p/gYcZy23JbFuDc4RKgulZr7ecRyEmTNEqBxe8lCr1fY8xPVpryqqi\nbb00zLk9PKup6DdLHh7kBOOIs+WWbd2w1YZPXixIf/YFwyLhjeMhWRqTlRVZqjDCIpRDCEeeRWRh\nwLqsaTqDCEJvuN5Df69nRj/yeIFCFAYke9ZtGgmkCJjkCabvMEaTpRlhGOCcRSoIAsd8adhVNXmk\nmBQRTdPhUuUVN0rSaf3KXiVf8YQE/sx4vZlR7raEecE/+6/+OY8/+pB/+6/+T5ptxY17bzK68xZB\nuUEUObW2DI7ukBRjQuG4OH1OoiR37r3FYDTg4vKS6eEBxfQAJ1/wYlFRlyVxFuOEh4dZ4+h6S2ed\nRz3GIZ3uiYucw6Mpttd0VUWzh4nhDEmokBLSOGCYxWgrme8qdp2mN9AJi2kNsoc4jDGRIYgi4iRB\nCEUYRhRFgela0nzAaDT1e31nMX1H53mRWAHb3ZpawHg8Js1ykrwgCiJCFdE5T5JwFvquoy63yL5h\nkMckAmTdM9+0VI3Zd7adP5NUgmmecDgsyNPUH4kIgQoShuND3v/WA2YXF3zx7AVoS4/kfLakXf6a\nrqqJwqlfzfUtRmiiMCPPCu5Oct44PqLIJgRhjlAxqBDjFJ3WrGrDsvKvb1dXLHZr0jhHW0jikFES\nMkwSjoYDBknMz9rmt5XfNxendYJ1C5u6YVP3LKqeYZ6gcZwvS3at9phH4zC2p241lzuHygN/QN60\nGGPQvZ8xda/RXUdVl2it6Xu9Z8s4bNeSuZpJBK0xpIH0M48zNL3l10+vSP76U/70/fukccDFtuF8\nU7NqDVVnsNZ5KncmKJueqush8NjC3loM3nRdN403XytFFIZIFVKkCcejjEQKEIpBlrHa1Xxxes5w\n4FEWzloPvep7LmYrNtstaeT5uVfLNWHdoouUSjvKVlN1xpPLjeH84pwvvvicJE6oyi2b1RV1taFu\nKu7cfZO77/6Ajz75mM9/8XfM1zvkxQxtOvptyeToFmEyZbfrWG3m1J1hOh0hwgHzrWax7cknMYEJ\n2O5alo1h0/S4rfZSNKWoOsu2Nf7SdS2rrqMLFIu2p1vusNojZOpdyWzjKQHa2H1xhuRJum/ChGyN\n9QIPa1mu1jw7PSUJAgyWqtqxaTuWZU3bdUil6IWgcY4Ej/5oyg3VbkOjO8qupelairxgPJ0QG82q\nKgk2W5qnZyS7kjrsmV3MmV2c8+v5C54+OScJIMsSCGHXGi42HavKeFug8zJM3Wsu5nPiLCOJE8+R\nlXL/OXaIdMD9B29zttyxvZwTSENbV2zaNW1dY1SBMwpjWrA9SmUEWCJ6Lmdz6rpkuRpTd47xoQYR\nUFUVX56t2JQ1dVNRty2XqxXj3BAoRW8MVdcyTrx0Mg0DovC3l6D4JoZJkYRuksaewmcdrYEo8DzY\nputp+lcU8kBK4igkjFPvfbzmeO6Rg/b6qzFYZ/coR4twAussRRrz4+99i2GWesK5gVXd0Wh7PdGR\nxQE3JgVpHFI3DbtGM9v51ry8FrHv1SLW+b0SwiMktfX7pL7XPH/2DNuUhEpikeRpwrhIiaTHcqAC\nksTDpMIwREnlgVb7+2jbhtlyTW8NQkDddggpCZXy3CEVcHzjJioIuLi84u7tE+7fu+tjIax7OSvr\nvidJc6QUzC/OWF5eEEQxcZp6BauUJElG12mauqTvGoRwpGnGaDjG9D273YYsz5FK8ekXz2hax/Tg\nYH+0AeypcP4IydKUJcuLU4ok4uhwihQSnGGURBwOC+qy4my2YLYpqbsevXfY9cbQ9oYkLzg+uc2u\nrLBdxxu3TrwlKlCgNbvNmvly5bWoQhBHIYeHR2R5AUC3dyn1xngYm4A4TYmThDgMPfzKQpamFIWH\nlp8tFnz84YfIvqNtWuJQkiURkZJgjX9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7WWN77Y3uQjCZHPDe+9/j+fNn5E9f8PxyxunZ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P4W8MeXSNbkouXn+OG1WcPn/COspJckVWmYQmyyvSKMPrK7q2RdZUNFWF1FBVJYNW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\naLxOztfxOv5E43Vyvo7X8Sca/w8RjDmAHslO0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzQo1JcxYTIh",
        "colab_type": "text"
      },
      "source": [
        "Previewing the data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_cLElNcfS01",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class VAE(nn.Module):\n",
        "    def __init__(self, nc, ndf, ngf, z_dim=150):\n",
        "        super(VAE, self).__init__()\n",
        "        h_dim = nc * ndf * ngf\n",
        "        self.nc = nc\n",
        "        self.ndf = ndf\n",
        "        self.ngf = ngf\n",
        "        self.h_dim = h_dim\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Linear(h_dim, 2000),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(2000, 500),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(500, 100),\n",
        "            nn.ReLU()\n",
        "        )   \n",
        "        \n",
        "        self.fc1 = nn.Linear(100, z_dim)\n",
        "        self.fc2 = nn.Linear(100, z_dim)\n",
        "        \n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.Linear(z_dim, 100),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(100, 500),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(500, 2000),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(2000, h_dim),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "        \n",
        "    def reparameterize(self, mu, logvar):\n",
        "        std = logvar.mul(0.5).exp_().to(device)\n",
        "        esp = torch.randn(*mu.size()).to(device)\n",
        "        z = mu + std * esp\n",
        "        return z\n",
        "    \n",
        "    def bottleneck(self, h):\n",
        "        mu, logvar = self.fc1(h), self.fc2(h)\n",
        "        z = self.reparameterize(mu, logvar)\n",
        "        return z, mu, logvar\n",
        "\n",
        "    def encode(self, x):\n",
        "        # print(\"encoder input shape\" , x.shape)\n",
        "        h = self.encoder(x)\n",
        "        z, mu, logvar = self.bottleneck(h)\n",
        "        return z, mu, logvar\n",
        "\n",
        "    def decode(self, z):\n",
        "        z = self.decoder(z)\n",
        "        return z\n",
        "\n",
        "    def forward(self, x):\n",
        "        \n",
        "        # print(x.shape, \"input shape\")\n",
        "        x = x.view(-1, self.nc * self.ndf * self.ngf)\n",
        "        # print(\"reshape\", x.shape)\n",
        "        z, mu, logvar = self.encode(x)\n",
        "        # print(\"resampleing\" , z.shape)\n",
        "        z = self.decode(z)\n",
        "        # print(z.shape, \"output shape\")\n",
        "        z = z.view(-1, self.nc, self.ndf , self.ngf )\n",
        "        return z, mu, logvar\n",
        "\n",
        "\n",
        "batch, _ = next(iter(data_loader))\n",
        "img = batch[1]\n",
        "\n",
        "channels = img.size(0)\n",
        "w, h = img.size(1) , img.size(2) \n",
        "\n",
        "nc = channels\n",
        "ndf = w\n",
        "ngf = h\n",
        "\n",
        "model = VAE(nc=nc, ngf=ngf, ndf=ndf).to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=.0005)\n",
        "\n",
        "def loss_fn(recon_x, x, mu, logvar):\n",
        "    BCE = F.binary_cross_entropy(recon_x, x, reduction='sum')\n",
        "    # BCE = F.mse_loss(recon_x, x, size_average=False)\n",
        "\n",
        "    # see Appendix B from VAE paper:\n",
        "    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n",
        "    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n",
        "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
        "\n",
        "    return BCE + KLD\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1f6uvQjMDgtk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(epoch):\n",
        "    model.train()\n",
        "    train_loss = 0\n",
        "    count_data = 0\n",
        "    batch_count_data = 0\n",
        "    for batch_idx, (data, _) in enumerate(data_loader):\n",
        "        # print(batch_idx)\n",
        "        count_data += len(data)\n",
        "        batch_count_data += len(data)\n",
        "        images = data.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        recon_batch, mu, log_var = model(images)\n",
        "        loss = loss_fn(recon_batch, images, mu, log_var)\n",
        "        \n",
        "        loss.backward()\n",
        "        train_loss += loss.item()\n",
        "        optimizer.step()\n",
        "        \n",
        "        if batch_idx % 10 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), dataset_len,\n",
        "                100. * batch_idx / len(data_loader), loss.item() / batch_count_data))\n",
        "            batch_count_data = 0\n",
        "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / count_data))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkYVgfoJYgBl",
        "colab_type": "text"
      },
      "source": [
        "Setting up the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcVLmdsNzS2c",
        "colab_type": "code",
        "outputId": "c14f1669-526b-4fb7-e53e-0232365ee4c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(50):\n",
        "  train(i)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [0/100000 (0%)]\tLoss: 8521.380859\n",
            "Train Epoch: 0 [2560/100000 (3%)]\tLoss: -5409.062891\n",
            "Train Epoch: 0 [5120/100000 (5%)]\tLoss: -7084.942969\n",
            "Train Epoch: 0 [7680/100000 (8%)]\tLoss: -9471.921875\n",
            "Train Epoch: 0 [10240/100000 (10%)]\tLoss: -9879.845312\n",
            "Train Epoch: 0 [12800/100000 (13%)]\tLoss: -8805.605469\n",
            "Train Epoch: 0 [15360/100000 (15%)]\tLoss: -11231.578906\n",
            "Train Epoch: 0 [17920/100000 (18%)]\tLoss: -11479.396875\n",
            "Train Epoch: 0 [20480/100000 (20%)]\tLoss: -9992.720312\n",
            "Train Epoch: 0 [23040/100000 (23%)]\tLoss: -11486.210156\n",
            "Train Epoch: 0 [25600/100000 (26%)]\tLoss: -10478.250000\n",
            "Train Epoch: 0 [28160/100000 (28%)]\tLoss: -10928.394531\n",
            "Train Epoch: 0 [30720/100000 (31%)]\tLoss: -10362.338281\n",
            "Train Epoch: 0 [33280/100000 (33%)]\tLoss: -10179.536719\n",
            "Train Epoch: 0 [35840/100000 (36%)]\tLoss: -10677.310156\n",
            "Train Epoch: 0 [38400/100000 (38%)]\tLoss: -11705.994531\n",
            "Train Epoch: 0 [40960/100000 (41%)]\tLoss: -10970.619531\n",
            "Train Epoch: 0 [43520/100000 (43%)]\tLoss: -11473.989063\n",
            "Train Epoch: 0 [46080/100000 (46%)]\tLoss: -11683.025781\n",
            "Train Epoch: 0 [48640/100000 (49%)]\tLoss: -10490.800000\n",
            "Train Epoch: 0 [51200/100000 (51%)]\tLoss: -10635.318750\n",
            "Train Epoch: 0 [53760/100000 (54%)]\tLoss: -11034.624219\n",
            "Train Epoch: 0 [56320/100000 (56%)]\tLoss: -10855.981250\n",
            "Train Epoch: 0 [58880/100000 (59%)]\tLoss: -11462.839062\n",
            "Train Epoch: 0 [61440/100000 (61%)]\tLoss: -10773.587500\n",
            "Train Epoch: 0 [64000/100000 (64%)]\tLoss: -10617.312500\n",
            "Train Epoch: 0 [66560/100000 (66%)]\tLoss: -10827.047656\n",
            "Train Epoch: 0 [69120/100000 (69%)]\tLoss: -10932.805469\n",
            "Train Epoch: 0 [71680/100000 (72%)]\tLoss: -10588.103906\n",
            "Train Epoch: 0 [74240/100000 (74%)]\tLoss: -9707.144531\n",
            "Train Epoch: 0 [76800/100000 (77%)]\tLoss: -9961.790625\n",
            "Train Epoch: 0 [79360/100000 (79%)]\tLoss: -10641.377344\n",
            "Train Epoch: 0 [81920/100000 (82%)]\tLoss: -11113.560938\n",
            "Train Epoch: 0 [84480/100000 (84%)]\tLoss: -10569.441406\n",
            "Train Epoch: 0 [87040/100000 (87%)]\tLoss: -10353.160938\n",
            "Train Epoch: 0 [89600/100000 (90%)]\tLoss: -10879.282031\n",
            "Train Epoch: 0 [92160/100000 (92%)]\tLoss: -10938.950781\n",
            "Train Epoch: 0 [94720/100000 (95%)]\tLoss: -10854.890625\n",
            "Train Epoch: 0 [97280/100000 (97%)]\tLoss: -10934.610937\n",
            "Train Epoch: 0 [62400/100000 (100%)]\tLoss: -8232.535714\n",
            "====> Epoch: 0 Average loss: -98407.5053\n",
            "Train Epoch: 1 [0/100000 (0%)]\tLoss: -111936.179688\n",
            "Train Epoch: 1 [2560/100000 (3%)]\tLoss: -10953.199219\n",
            "Train Epoch: 1 [5120/100000 (5%)]\tLoss: -11402.211719\n",
            "Train Epoch: 1 [7680/100000 (8%)]\tLoss: -11726.531250\n",
            "Train Epoch: 1 [10240/100000 (10%)]\tLoss: -10811.662500\n",
            "Train Epoch: 1 [12800/100000 (13%)]\tLoss: -11113.371094\n",
            "Train Epoch: 1 [15360/100000 (15%)]\tLoss: -12515.949219\n",
            "Train Epoch: 1 [17920/100000 (18%)]\tLoss: -11582.563281\n",
            "Train Epoch: 1 [20480/100000 (20%)]\tLoss: -11934.012500\n",
            "Train Epoch: 1 [23040/100000 (23%)]\tLoss: -10999.699219\n",
            "Train Epoch: 1 [25600/100000 (26%)]\tLoss: -11717.914062\n",
            "Train Epoch: 1 [28160/100000 (28%)]\tLoss: -11874.936719\n",
            "Train Epoch: 1 [30720/100000 (31%)]\tLoss: -11137.135937\n",
            "Train Epoch: 1 [33280/100000 (33%)]\tLoss: -12001.288281\n",
            "Train Epoch: 1 [35840/100000 (36%)]\tLoss: -11627.317969\n",
            "Train Epoch: 1 [38400/100000 (38%)]\tLoss: -12014.952344\n",
            "Train Epoch: 1 [40960/100000 (41%)]\tLoss: -11574.943750\n",
            "Train Epoch: 1 [43520/100000 (43%)]\tLoss: -11918.120312\n",
            "Train Epoch: 1 [46080/100000 (46%)]\tLoss: -11737.823437\n",
            "Train Epoch: 1 [48640/100000 (49%)]\tLoss: -11949.379688\n",
            "Train Epoch: 1 [51200/100000 (51%)]\tLoss: -12142.531250\n",
            "Train Epoch: 1 [53760/100000 (54%)]\tLoss: -11819.581250\n",
            "Train Epoch: 1 [56320/100000 (56%)]\tLoss: -11757.069531\n",
            "Train Epoch: 1 [58880/100000 (59%)]\tLoss: -12331.662500\n",
            "Train Epoch: 1 [61440/100000 (61%)]\tLoss: -11746.324219\n",
            "Train Epoch: 1 [64000/100000 (64%)]\tLoss: -11705.049219\n",
            "Train Epoch: 1 [66560/100000 (66%)]\tLoss: -12048.501562\n",
            "Train Epoch: 1 [69120/100000 (69%)]\tLoss: -12094.128906\n",
            "Train Epoch: 1 [71680/100000 (72%)]\tLoss: -12163.928125\n",
            "Train Epoch: 1 [74240/100000 (74%)]\tLoss: -11576.341406\n",
            "Train Epoch: 1 [76800/100000 (77%)]\tLoss: -12428.242969\n",
            "Train Epoch: 1 [79360/100000 (79%)]\tLoss: -12682.282031\n",
            "Train Epoch: 1 [81920/100000 (82%)]\tLoss: -13008.846094\n",
            "Train Epoch: 1 [84480/100000 (84%)]\tLoss: -12537.711719\n",
            "Train Epoch: 1 [87040/100000 (87%)]\tLoss: -12119.436719\n",
            "Train Epoch: 1 [89600/100000 (90%)]\tLoss: -11408.792187\n",
            "Train Epoch: 1 [92160/100000 (92%)]\tLoss: -12797.996094\n",
            "Train Epoch: 1 [94720/100000 (95%)]\tLoss: -12214.367969\n",
            "Train Epoch: 1 [97280/100000 (97%)]\tLoss: -12103.109375\n",
            "Train Epoch: 1 [62400/100000 (100%)]\tLoss: -8832.117695\n",
            "====> Epoch: 1 Average loss: -118941.0722\n",
            "Train Epoch: 2 [0/100000 (0%)]\tLoss: -124709.226562\n",
            "Train Epoch: 2 [2560/100000 (3%)]\tLoss: -12468.300000\n",
            "Train Epoch: 2 [5120/100000 (5%)]\tLoss: -12388.060156\n",
            "Train Epoch: 2 [7680/100000 (8%)]\tLoss: -13020.104687\n",
            "Train Epoch: 2 [10240/100000 (10%)]\tLoss: -13561.007812\n",
            "Train Epoch: 2 [12800/100000 (13%)]\tLoss: -12420.796094\n",
            "Train Epoch: 2 [15360/100000 (15%)]\tLoss: -12415.575000\n",
            "Train Epoch: 2 [17920/100000 (18%)]\tLoss: -12441.322656\n",
            "Train Epoch: 2 [20480/100000 (20%)]\tLoss: -12592.224219\n",
            "Train Epoch: 2 [23040/100000 (23%)]\tLoss: -12694.159375\n",
            "Train Epoch: 2 [25600/100000 (26%)]\tLoss: -12100.632812\n",
            "Train Epoch: 2 [28160/100000 (28%)]\tLoss: -13076.281250\n",
            "Train Epoch: 2 [30720/100000 (31%)]\tLoss: -12876.417187\n",
            "Train Epoch: 2 [33280/100000 (33%)]\tLoss: -13016.644531\n",
            "Train Epoch: 2 [35840/100000 (36%)]\tLoss: -13270.881250\n",
            "Train Epoch: 2 [38400/100000 (38%)]\tLoss: -13143.620312\n",
            "Train Epoch: 2 [40960/100000 (41%)]\tLoss: -12468.382031\n",
            "Train Epoch: 2 [43520/100000 (43%)]\tLoss: -13618.804688\n",
            "Train Epoch: 2 [46080/100000 (46%)]\tLoss: -13515.640625\n",
            "Train Epoch: 2 [48640/100000 (49%)]\tLoss: -13733.021875\n",
            "Train Epoch: 2 [51200/100000 (51%)]\tLoss: -13881.304688\n",
            "Train Epoch: 2 [53760/100000 (54%)]\tLoss: -13993.329687\n",
            "Train Epoch: 2 [56320/100000 (56%)]\tLoss: -13146.940625\n",
            "Train Epoch: 2 [58880/100000 (59%)]\tLoss: -13001.025781\n",
            "Train Epoch: 2 [61440/100000 (61%)]\tLoss: -13926.678125\n",
            "Train Epoch: 2 [64000/100000 (64%)]\tLoss: -12712.971875\n",
            "Train Epoch: 2 [66560/100000 (66%)]\tLoss: -13048.076563\n",
            "Train Epoch: 2 [69120/100000 (69%)]\tLoss: -13716.915625\n",
            "Train Epoch: 2 [71680/100000 (72%)]\tLoss: -14345.100000\n",
            "Train Epoch: 2 [74240/100000 (74%)]\tLoss: -13878.142187\n",
            "Train Epoch: 2 [76800/100000 (77%)]\tLoss: -13207.225000\n",
            "Train Epoch: 2 [79360/100000 (79%)]\tLoss: -13237.485937\n",
            "Train Epoch: 2 [81920/100000 (82%)]\tLoss: -14267.443750\n",
            "Train Epoch: 2 [84480/100000 (84%)]\tLoss: -12865.439844\n",
            "Train Epoch: 2 [87040/100000 (87%)]\tLoss: -13316.025000\n",
            "Train Epoch: 2 [89600/100000 (90%)]\tLoss: -15020.315625\n",
            "Train Epoch: 2 [92160/100000 (92%)]\tLoss: -14375.546875\n",
            "Train Epoch: 2 [94720/100000 (95%)]\tLoss: -13760.903125\n",
            "Train Epoch: 2 [97280/100000 (97%)]\tLoss: -14065.517187\n",
            "Train Epoch: 2 [62400/100000 (100%)]\tLoss: -9138.413149\n",
            "====> Epoch: 2 Average loss: -132403.5688\n",
            "Train Epoch: 3 [0/100000 (0%)]\tLoss: -138486.531250\n",
            "Train Epoch: 3 [2560/100000 (3%)]\tLoss: -13567.564062\n",
            "Train Epoch: 3 [5120/100000 (5%)]\tLoss: -13893.471875\n",
            "Train Epoch: 3 [7680/100000 (8%)]\tLoss: -13736.553125\n",
            "Train Epoch: 3 [10240/100000 (10%)]\tLoss: -13912.256250\n",
            "Train Epoch: 3 [12800/100000 (13%)]\tLoss: -13512.778125\n",
            "Train Epoch: 3 [15360/100000 (15%)]\tLoss: -14287.604687\n",
            "Train Epoch: 3 [17920/100000 (18%)]\tLoss: -13911.414062\n",
            "Train Epoch: 3 [20480/100000 (20%)]\tLoss: -13804.267187\n",
            "Train Epoch: 3 [23040/100000 (23%)]\tLoss: -13481.884375\n",
            "Train Epoch: 3 [25600/100000 (26%)]\tLoss: -14130.629688\n",
            "Train Epoch: 3 [28160/100000 (28%)]\tLoss: -13425.089062\n",
            "Train Epoch: 3 [30720/100000 (31%)]\tLoss: -13222.546875\n",
            "Train Epoch: 3 [33280/100000 (33%)]\tLoss: -13090.878906\n",
            "Train Epoch: 3 [35840/100000 (36%)]\tLoss: -13299.117188\n",
            "Train Epoch: 3 [38400/100000 (38%)]\tLoss: -13190.743750\n",
            "Train Epoch: 3 [40960/100000 (41%)]\tLoss: -13178.726562\n",
            "Train Epoch: 3 [43520/100000 (43%)]\tLoss: -14051.556250\n",
            "Train Epoch: 3 [46080/100000 (46%)]\tLoss: -13782.239063\n",
            "Train Epoch: 3 [48640/100000 (49%)]\tLoss: -13196.110937\n",
            "Train Epoch: 3 [51200/100000 (51%)]\tLoss: -14775.818750\n",
            "Train Epoch: 3 [53760/100000 (54%)]\tLoss: -14300.560938\n",
            "Train Epoch: 3 [56320/100000 (56%)]\tLoss: -14333.618750\n",
            "Train Epoch: 3 [58880/100000 (59%)]\tLoss: -13593.460938\n",
            "Train Epoch: 3 [61440/100000 (61%)]\tLoss: -13991.082813\n",
            "Train Epoch: 3 [64000/100000 (64%)]\tLoss: -14206.876562\n",
            "Train Epoch: 3 [66560/100000 (66%)]\tLoss: -13280.095312\n",
            "Train Epoch: 3 [69120/100000 (69%)]\tLoss: -14538.214062\n",
            "Train Epoch: 3 [71680/100000 (72%)]\tLoss: -13034.112500\n",
            "Train Epoch: 3 [74240/100000 (74%)]\tLoss: -13747.979687\n",
            "Train Epoch: 3 [76800/100000 (77%)]\tLoss: -13944.479687\n",
            "Train Epoch: 3 [79360/100000 (79%)]\tLoss: -15285.645313\n",
            "Train Epoch: 3 [81920/100000 (82%)]\tLoss: -14763.835938\n",
            "Train Epoch: 3 [84480/100000 (84%)]\tLoss: -13504.704687\n",
            "Train Epoch: 3 [87040/100000 (87%)]\tLoss: -14089.071875\n",
            "Train Epoch: 3 [89600/100000 (90%)]\tLoss: -13944.626562\n",
            "Train Epoch: 3 [92160/100000 (92%)]\tLoss: -13730.093750\n",
            "Train Epoch: 3 [94720/100000 (95%)]\tLoss: -14247.245312\n",
            "Train Epoch: 3 [97280/100000 (97%)]\tLoss: -14338.345312\n",
            "Train Epoch: 3 [62400/100000 (100%)]\tLoss: -9945.440747\n",
            "====> Epoch: 3 Average loss: -139094.7542\n",
            "Train Epoch: 4 [0/100000 (0%)]\tLoss: -146031.171875\n",
            "Train Epoch: 4 [2560/100000 (3%)]\tLoss: -14025.896875\n",
            "Train Epoch: 4 [5120/100000 (5%)]\tLoss: -13906.021875\n",
            "Train Epoch: 4 [7680/100000 (8%)]\tLoss: -13976.317188\n",
            "Train Epoch: 4 [10240/100000 (10%)]\tLoss: -15000.300000\n",
            "Train Epoch: 4 [12800/100000 (13%)]\tLoss: -13831.264063\n",
            "Train Epoch: 4 [15360/100000 (15%)]\tLoss: -14607.968750\n",
            "Train Epoch: 4 [17920/100000 (18%)]\tLoss: -15142.404688\n",
            "Train Epoch: 4 [20480/100000 (20%)]\tLoss: -14821.253125\n",
            "Train Epoch: 4 [23040/100000 (23%)]\tLoss: -14910.287500\n",
            "Train Epoch: 4 [25600/100000 (26%)]\tLoss: -13899.757812\n",
            "Train Epoch: 4 [28160/100000 (28%)]\tLoss: -15087.462500\n",
            "Train Epoch: 4 [30720/100000 (31%)]\tLoss: -13867.406250\n",
            "Train Epoch: 4 [33280/100000 (33%)]\tLoss: -14091.364063\n",
            "Train Epoch: 4 [35840/100000 (36%)]\tLoss: -14153.235937\n",
            "Train Epoch: 4 [38400/100000 (38%)]\tLoss: -13917.825000\n",
            "Train Epoch: 4 [40960/100000 (41%)]\tLoss: -13864.801563\n",
            "Train Epoch: 4 [43520/100000 (43%)]\tLoss: -13796.709375\n",
            "Train Epoch: 4 [46080/100000 (46%)]\tLoss: -14465.160938\n",
            "Train Epoch: 4 [48640/100000 (49%)]\tLoss: -14544.285938\n",
            "Train Epoch: 4 [51200/100000 (51%)]\tLoss: -13475.423437\n",
            "Train Epoch: 4 [53760/100000 (54%)]\tLoss: -13809.412500\n",
            "Train Epoch: 4 [56320/100000 (56%)]\tLoss: -13842.131250\n",
            "Train Epoch: 4 [58880/100000 (59%)]\tLoss: -14012.943750\n",
            "Train Epoch: 4 [61440/100000 (61%)]\tLoss: -15042.201563\n",
            "Train Epoch: 4 [64000/100000 (64%)]\tLoss: -13731.718750\n",
            "Train Epoch: 4 [66560/100000 (66%)]\tLoss: -14166.248438\n",
            "Train Epoch: 4 [69120/100000 (69%)]\tLoss: -13707.228125\n",
            "Train Epoch: 4 [71680/100000 (72%)]\tLoss: -14089.425000\n",
            "Train Epoch: 4 [74240/100000 (74%)]\tLoss: -13117.325000\n",
            "Train Epoch: 4 [76800/100000 (77%)]\tLoss: -14454.664062\n",
            "Train Epoch: 4 [79360/100000 (79%)]\tLoss: -14176.165625\n",
            "Train Epoch: 4 [81920/100000 (82%)]\tLoss: -13663.251562\n",
            "Train Epoch: 4 [84480/100000 (84%)]\tLoss: -14044.387500\n",
            "Train Epoch: 4 [87040/100000 (87%)]\tLoss: -12580.514844\n",
            "Train Epoch: 4 [89600/100000 (90%)]\tLoss: -13025.164844\n",
            "Train Epoch: 4 [92160/100000 (92%)]\tLoss: -14367.395313\n",
            "Train Epoch: 4 [94720/100000 (95%)]\tLoss: -14519.915625\n",
            "Train Epoch: 4 [97280/100000 (97%)]\tLoss: -13632.284375\n",
            "Train Epoch: 4 [62400/100000 (100%)]\tLoss: -9302.116883\n",
            "====> Epoch: 4 Average loss: -139371.8146\n",
            "Train Epoch: 5 [0/100000 (0%)]\tLoss: -134095.578125\n",
            "Train Epoch: 5 [2560/100000 (3%)]\tLoss: -13625.100000\n",
            "Train Epoch: 5 [5120/100000 (5%)]\tLoss: -13277.923437\n",
            "Train Epoch: 5 [7680/100000 (8%)]\tLoss: -14114.535938\n",
            "Train Epoch: 5 [10240/100000 (10%)]\tLoss: -13900.804688\n",
            "Train Epoch: 5 [12800/100000 (13%)]\tLoss: -13940.489063\n",
            "Train Epoch: 5 [15360/100000 (15%)]\tLoss: -13653.415625\n",
            "Train Epoch: 5 [17920/100000 (18%)]\tLoss: -14153.501562\n",
            "Train Epoch: 5 [20480/100000 (20%)]\tLoss: -13245.096875\n",
            "Train Epoch: 5 [23040/100000 (23%)]\tLoss: -13347.603125\n",
            "Train Epoch: 5 [25600/100000 (26%)]\tLoss: -13300.929688\n",
            "Train Epoch: 5 [28160/100000 (28%)]\tLoss: -13697.521875\n",
            "Train Epoch: 5 [30720/100000 (31%)]\tLoss: -13666.292187\n",
            "Train Epoch: 5 [33280/100000 (33%)]\tLoss: -13819.440625\n",
            "Train Epoch: 5 [35840/100000 (36%)]\tLoss: -12596.362500\n",
            "Train Epoch: 5 [38400/100000 (38%)]\tLoss: -13854.009375\n",
            "Train Epoch: 5 [40960/100000 (41%)]\tLoss: -13933.446875\n",
            "Train Epoch: 5 [43520/100000 (43%)]\tLoss: -14687.800000\n",
            "Train Epoch: 5 [46080/100000 (46%)]\tLoss: -13923.296875\n",
            "Train Epoch: 5 [48640/100000 (49%)]\tLoss: -13897.568750\n",
            "Train Epoch: 5 [51200/100000 (51%)]\tLoss: -13242.856250\n",
            "Train Epoch: 5 [53760/100000 (54%)]\tLoss: -14594.370312\n",
            "Train Epoch: 5 [56320/100000 (56%)]\tLoss: -13230.529688\n",
            "Train Epoch: 5 [58880/100000 (59%)]\tLoss: -13406.850000\n",
            "Train Epoch: 5 [61440/100000 (61%)]\tLoss: -13764.828125\n",
            "Train Epoch: 5 [64000/100000 (64%)]\tLoss: -13951.175000\n",
            "Train Epoch: 5 [66560/100000 (66%)]\tLoss: -13618.010937\n",
            "Train Epoch: 5 [69120/100000 (69%)]\tLoss: -14119.450000\n",
            "Train Epoch: 5 [71680/100000 (72%)]\tLoss: -14545.931250\n",
            "Train Epoch: 5 [74240/100000 (74%)]\tLoss: -13949.159375\n",
            "Train Epoch: 5 [76800/100000 (77%)]\tLoss: -12676.750781\n",
            "Train Epoch: 5 [79360/100000 (79%)]\tLoss: -13613.498438\n",
            "Train Epoch: 5 [81920/100000 (82%)]\tLoss: -12717.487500\n",
            "Train Epoch: 5 [84480/100000 (84%)]\tLoss: -13519.212500\n",
            "Train Epoch: 5 [87040/100000 (87%)]\tLoss: -13433.942188\n",
            "Train Epoch: 5 [89600/100000 (90%)]\tLoss: -13777.342188\n",
            "Train Epoch: 5 [92160/100000 (92%)]\tLoss: -13975.192188\n",
            "Train Epoch: 5 [94720/100000 (95%)]\tLoss: -13003.284375\n",
            "Train Epoch: 5 [97280/100000 (97%)]\tLoss: -13529.676563\n",
            "Train Epoch: 5 [62400/100000 (100%)]\tLoss: -9089.249188\n",
            "====> Epoch: 5 Average loss: -136365.5554\n",
            "Train Epoch: 6 [0/100000 (0%)]\tLoss: -137524.046875\n",
            "Train Epoch: 6 [2560/100000 (3%)]\tLoss: -13113.060938\n",
            "Train Epoch: 6 [5120/100000 (5%)]\tLoss: -13722.221875\n",
            "Train Epoch: 6 [7680/100000 (8%)]\tLoss: -12932.241406\n",
            "Train Epoch: 6 [10240/100000 (10%)]\tLoss: -13352.423437\n",
            "Train Epoch: 6 [12800/100000 (13%)]\tLoss: -13340.800000\n",
            "Train Epoch: 6 [15360/100000 (15%)]\tLoss: -14170.768750\n",
            "Train Epoch: 6 [17920/100000 (18%)]\tLoss: -14099.557812\n",
            "Train Epoch: 6 [20480/100000 (20%)]\tLoss: -14323.831250\n",
            "Train Epoch: 6 [23040/100000 (23%)]\tLoss: -13421.973438\n",
            "Train Epoch: 6 [25600/100000 (26%)]\tLoss: -14045.089062\n",
            "Train Epoch: 6 [28160/100000 (28%)]\tLoss: -12404.975781\n",
            "Train Epoch: 6 [30720/100000 (31%)]\tLoss: -13943.192188\n",
            "Train Epoch: 6 [33280/100000 (33%)]\tLoss: -14103.231250\n",
            "Train Epoch: 6 [35840/100000 (36%)]\tLoss: -13264.323437\n",
            "Train Epoch: 6 [38400/100000 (38%)]\tLoss: -13856.681250\n",
            "Train Epoch: 6 [40960/100000 (41%)]\tLoss: -12494.782812\n",
            "Train Epoch: 6 [43520/100000 (43%)]\tLoss: -13306.009375\n",
            "Train Epoch: 6 [46080/100000 (46%)]\tLoss: -13718.173437\n",
            "Train Epoch: 6 [48640/100000 (49%)]\tLoss: -13568.459375\n",
            "Train Epoch: 6 [51200/100000 (51%)]\tLoss: -13234.854687\n",
            "Train Epoch: 6 [53760/100000 (54%)]\tLoss: -13004.167187\n",
            "Train Epoch: 6 [56320/100000 (56%)]\tLoss: -14289.223438\n",
            "Train Epoch: 6 [58880/100000 (59%)]\tLoss: -13336.504688\n",
            "Train Epoch: 6 [61440/100000 (61%)]\tLoss: -13575.576563\n",
            "Train Epoch: 6 [64000/100000 (64%)]\tLoss: -13321.954687\n",
            "Train Epoch: 6 [66560/100000 (66%)]\tLoss: -14139.231250\n",
            "Train Epoch: 6 [69120/100000 (69%)]\tLoss: -12952.786719\n",
            "Train Epoch: 6 [71680/100000 (72%)]\tLoss: -13039.359375\n",
            "Train Epoch: 6 [74240/100000 (74%)]\tLoss: -13940.060938\n",
            "Train Epoch: 6 [76800/100000 (77%)]\tLoss: -13221.112500\n",
            "Train Epoch: 6 [79360/100000 (79%)]\tLoss: -13678.154688\n",
            "Train Epoch: 6 [81920/100000 (82%)]\tLoss: -12999.184375\n",
            "Train Epoch: 6 [84480/100000 (84%)]\tLoss: -13884.676563\n",
            "Train Epoch: 6 [87040/100000 (87%)]\tLoss: -12914.553125\n",
            "Train Epoch: 6 [89600/100000 (90%)]\tLoss: -13328.403125\n",
            "Train Epoch: 6 [92160/100000 (92%)]\tLoss: -13088.021875\n",
            "Train Epoch: 6 [94720/100000 (95%)]\tLoss: -12963.581250\n",
            "Train Epoch: 6 [97280/100000 (97%)]\tLoss: -14193.781250\n",
            "Train Epoch: 6 [62400/100000 (100%)]\tLoss: -8807.617695\n",
            "====> Epoch: 6 Average loss: -135268.4859\n",
            "Train Epoch: 7 [0/100000 (0%)]\tLoss: -131123.109375\n",
            "Train Epoch: 7 [2560/100000 (3%)]\tLoss: -13061.071094\n",
            "Train Epoch: 7 [5120/100000 (5%)]\tLoss: -13481.048437\n",
            "Train Epoch: 7 [7680/100000 (8%)]\tLoss: -13755.143750\n",
            "Train Epoch: 7 [10240/100000 (10%)]\tLoss: -12906.594531\n",
            "Train Epoch: 7 [12800/100000 (13%)]\tLoss: -12902.789844\n",
            "Train Epoch: 7 [15360/100000 (15%)]\tLoss: -12981.567188\n",
            "Train Epoch: 7 [17920/100000 (18%)]\tLoss: -13501.670313\n",
            "Train Epoch: 7 [20480/100000 (20%)]\tLoss: -12928.574219\n",
            "Train Epoch: 7 [23040/100000 (23%)]\tLoss: -12886.317188\n",
            "Train Epoch: 7 [25600/100000 (26%)]\tLoss: -12999.464844\n",
            "Train Epoch: 7 [28160/100000 (28%)]\tLoss: -14286.960938\n",
            "Train Epoch: 7 [30720/100000 (31%)]\tLoss: -12955.524219\n",
            "Train Epoch: 7 [33280/100000 (33%)]\tLoss: -13546.514063\n",
            "Train Epoch: 7 [35840/100000 (36%)]\tLoss: -12863.862500\n",
            "Train Epoch: 7 [38400/100000 (38%)]\tLoss: -13872.709375\n",
            "Train Epoch: 7 [40960/100000 (41%)]\tLoss: -13400.843750\n",
            "Train Epoch: 7 [43520/100000 (43%)]\tLoss: -13105.486719\n",
            "Train Epoch: 7 [46080/100000 (46%)]\tLoss: -13638.782812\n",
            "Train Epoch: 7 [48640/100000 (49%)]\tLoss: -13331.075000\n",
            "Train Epoch: 7 [51200/100000 (51%)]\tLoss: -13513.615625\n",
            "Train Epoch: 7 [53760/100000 (54%)]\tLoss: -13616.065625\n",
            "Train Epoch: 7 [56320/100000 (56%)]\tLoss: -13583.298437\n",
            "Train Epoch: 7 [58880/100000 (59%)]\tLoss: -13146.214062\n",
            "Train Epoch: 7 [61440/100000 (61%)]\tLoss: -12712.711719\n",
            "Train Epoch: 7 [64000/100000 (64%)]\tLoss: -13331.621875\n",
            "Train Epoch: 7 [66560/100000 (66%)]\tLoss: -12920.546094\n",
            "Train Epoch: 7 [69120/100000 (69%)]\tLoss: -12526.903906\n",
            "Train Epoch: 7 [71680/100000 (72%)]\tLoss: -13365.187500\n",
            "Train Epoch: 7 [74240/100000 (74%)]\tLoss: -13119.184375\n",
            "Train Epoch: 7 [76800/100000 (77%)]\tLoss: -12824.079687\n",
            "Train Epoch: 7 [79360/100000 (79%)]\tLoss: -12557.856250\n",
            "Train Epoch: 7 [81920/100000 (82%)]\tLoss: -13617.371875\n",
            "Train Epoch: 7 [84480/100000 (84%)]\tLoss: -13579.587500\n",
            "Train Epoch: 7 [87040/100000 (87%)]\tLoss: -13362.642187\n",
            "Train Epoch: 7 [89600/100000 (90%)]\tLoss: -13881.685938\n",
            "Train Epoch: 7 [92160/100000 (92%)]\tLoss: -13489.454687\n",
            "Train Epoch: 7 [94720/100000 (95%)]\tLoss: -13334.451563\n",
            "Train Epoch: 7 [97280/100000 (97%)]\tLoss: -13218.415625\n",
            "Train Epoch: 7 [62400/100000 (100%)]\tLoss: -8759.387175\n",
            "====> Epoch: 7 Average loss: -134059.0509\n",
            "Train Epoch: 8 [0/100000 (0%)]\tLoss: -135538.890625\n",
            "Train Epoch: 8 [2560/100000 (3%)]\tLoss: -13394.496875\n",
            "Train Epoch: 8 [5120/100000 (5%)]\tLoss: -13534.421875\n",
            "Train Epoch: 8 [7680/100000 (8%)]\tLoss: -13693.640625\n",
            "Train Epoch: 8 [10240/100000 (10%)]\tLoss: -12978.771094\n",
            "Train Epoch: 8 [12800/100000 (13%)]\tLoss: -13088.910938\n",
            "Train Epoch: 8 [15360/100000 (15%)]\tLoss: -14305.731250\n",
            "Train Epoch: 8 [17920/100000 (18%)]\tLoss: -13126.790625\n",
            "Train Epoch: 8 [20480/100000 (20%)]\tLoss: -14600.284375\n",
            "Train Epoch: 8 [23040/100000 (23%)]\tLoss: -13208.917187\n",
            "Train Epoch: 8 [25600/100000 (26%)]\tLoss: -13812.867188\n",
            "Train Epoch: 8 [28160/100000 (28%)]\tLoss: -12947.468750\n",
            "Train Epoch: 8 [30720/100000 (31%)]\tLoss: -13478.951563\n",
            "Train Epoch: 8 [33280/100000 (33%)]\tLoss: -13049.799219\n",
            "Train Epoch: 8 [35840/100000 (36%)]\tLoss: -13949.143750\n",
            "Train Epoch: 8 [38400/100000 (38%)]\tLoss: -13272.209375\n",
            "Train Epoch: 8 [40960/100000 (41%)]\tLoss: -14266.896875\n",
            "Train Epoch: 8 [43520/100000 (43%)]\tLoss: -14008.401562\n",
            "Train Epoch: 8 [46080/100000 (46%)]\tLoss: -13795.510937\n",
            "Train Epoch: 8 [48640/100000 (49%)]\tLoss: -13329.026562\n",
            "Train Epoch: 8 [51200/100000 (51%)]\tLoss: -12844.490625\n",
            "Train Epoch: 8 [53760/100000 (54%)]\tLoss: -12563.010937\n",
            "Train Epoch: 8 [56320/100000 (56%)]\tLoss: -13160.706250\n",
            "Train Epoch: 8 [58880/100000 (59%)]\tLoss: -13283.487500\n",
            "Train Epoch: 8 [61440/100000 (61%)]\tLoss: -13154.756250\n",
            "Train Epoch: 8 [64000/100000 (64%)]\tLoss: -13066.085156\n",
            "Train Epoch: 8 [66560/100000 (66%)]\tLoss: -13624.753125\n",
            "Train Epoch: 8 [69120/100000 (69%)]\tLoss: -12500.651562\n",
            "Train Epoch: 8 [71680/100000 (72%)]\tLoss: -12832.445312\n",
            "Train Epoch: 8 [74240/100000 (74%)]\tLoss: -13239.081250\n",
            "Train Epoch: 8 [76800/100000 (77%)]\tLoss: -12926.016406\n",
            "Train Epoch: 8 [79360/100000 (79%)]\tLoss: -13237.909375\n",
            "Train Epoch: 8 [81920/100000 (82%)]\tLoss: -12822.750000\n",
            "Train Epoch: 8 [84480/100000 (84%)]\tLoss: -12701.897656\n",
            "Train Epoch: 8 [87040/100000 (87%)]\tLoss: -12935.891406\n",
            "Train Epoch: 8 [89600/100000 (90%)]\tLoss: -13907.740625\n",
            "Train Epoch: 8 [92160/100000 (92%)]\tLoss: -12599.046875\n",
            "Train Epoch: 8 [94720/100000 (95%)]\tLoss: -13196.000000\n",
            "Train Epoch: 8 [97280/100000 (97%)]\tLoss: -13811.706250\n",
            "Train Epoch: 8 [62400/100000 (100%)]\tLoss: -7860.950487\n",
            "====> Epoch: 8 Average loss: -133733.3520\n",
            "Train Epoch: 9 [0/100000 (0%)]\tLoss: -128859.125000\n",
            "Train Epoch: 9 [2560/100000 (3%)]\tLoss: -13208.120312\n",
            "Train Epoch: 9 [5120/100000 (5%)]\tLoss: -12868.200781\n",
            "Train Epoch: 9 [7680/100000 (8%)]\tLoss: -12694.356250\n",
            "Train Epoch: 9 [10240/100000 (10%)]\tLoss: -13973.215625\n",
            "Train Epoch: 9 [12800/100000 (13%)]\tLoss: -12773.056250\n",
            "Train Epoch: 9 [15360/100000 (15%)]\tLoss: -13150.329687\n",
            "Train Epoch: 9 [17920/100000 (18%)]\tLoss: -13118.175000\n",
            "Train Epoch: 9 [20480/100000 (20%)]\tLoss: -13323.067188\n",
            "Train Epoch: 9 [23040/100000 (23%)]\tLoss: -12756.122656\n",
            "Train Epoch: 9 [25600/100000 (26%)]\tLoss: -13743.973438\n",
            "Train Epoch: 9 [28160/100000 (28%)]\tLoss: -13360.853125\n",
            "Train Epoch: 9 [30720/100000 (31%)]\tLoss: -12622.673437\n",
            "Train Epoch: 9 [33280/100000 (33%)]\tLoss: -13433.929688\n",
            "Train Epoch: 9 [35840/100000 (36%)]\tLoss: -13074.785156\n",
            "Train Epoch: 9 [38400/100000 (38%)]\tLoss: -13527.643750\n",
            "Train Epoch: 9 [40960/100000 (41%)]\tLoss: -12842.570312\n",
            "Train Epoch: 9 [43520/100000 (43%)]\tLoss: -12814.089062\n",
            "Train Epoch: 9 [46080/100000 (46%)]\tLoss: -13999.928125\n",
            "Train Epoch: 9 [48640/100000 (49%)]\tLoss: -13490.551563\n",
            "Train Epoch: 9 [51200/100000 (51%)]\tLoss: -12918.536719\n",
            "Train Epoch: 9 [53760/100000 (54%)]\tLoss: -13209.290625\n",
            "Train Epoch: 9 [56320/100000 (56%)]\tLoss: -14340.931250\n",
            "Train Epoch: 9 [58880/100000 (59%)]\tLoss: -13594.906250\n",
            "Train Epoch: 9 [61440/100000 (61%)]\tLoss: -13886.042187\n",
            "Train Epoch: 9 [64000/100000 (64%)]\tLoss: -13024.449219\n",
            "Train Epoch: 9 [66560/100000 (66%)]\tLoss: -13177.706250\n",
            "Train Epoch: 9 [69120/100000 (69%)]\tLoss: -13491.829687\n",
            "Train Epoch: 9 [71680/100000 (72%)]\tLoss: -13133.582813\n",
            "Train Epoch: 9 [74240/100000 (74%)]\tLoss: -12963.411719\n",
            "Train Epoch: 9 [76800/100000 (77%)]\tLoss: -13026.671875\n",
            "Train Epoch: 9 [79360/100000 (79%)]\tLoss: -13074.665625\n",
            "Train Epoch: 9 [81920/100000 (82%)]\tLoss: -13014.394531\n",
            "Train Epoch: 9 [84480/100000 (84%)]\tLoss: -12834.135156\n",
            "Train Epoch: 9 [87040/100000 (87%)]\tLoss: -12766.488281\n",
            "Train Epoch: 9 [89600/100000 (90%)]\tLoss: -13661.410938\n",
            "Train Epoch: 9 [92160/100000 (92%)]\tLoss: -12992.173437\n",
            "Train Epoch: 9 [94720/100000 (95%)]\tLoss: -12836.410156\n",
            "Train Epoch: 9 [97280/100000 (97%)]\tLoss: -13781.646875\n",
            "Train Epoch: 9 [62400/100000 (100%)]\tLoss: -8885.853084\n",
            "====> Epoch: 9 Average loss: -131921.0686\n",
            "Train Epoch: 10 [0/100000 (0%)]\tLoss: -135803.250000\n",
            "Train Epoch: 10 [2560/100000 (3%)]\tLoss: -13099.944531\n",
            "Train Epoch: 10 [5120/100000 (5%)]\tLoss: -12952.694531\n",
            "Train Epoch: 10 [7680/100000 (8%)]\tLoss: -12561.142187\n",
            "Train Epoch: 10 [10240/100000 (10%)]\tLoss: -13098.088281\n",
            "Train Epoch: 10 [12800/100000 (13%)]\tLoss: -12805.279688\n",
            "Train Epoch: 10 [15360/100000 (15%)]\tLoss: -13402.951563\n",
            "Train Epoch: 10 [17920/100000 (18%)]\tLoss: -13321.853125\n",
            "Train Epoch: 10 [20480/100000 (20%)]\tLoss: -12875.200000\n",
            "Train Epoch: 10 [23040/100000 (23%)]\tLoss: -12530.646094\n",
            "Train Epoch: 10 [25600/100000 (26%)]\tLoss: -12826.838281\n",
            "Train Epoch: 10 [28160/100000 (28%)]\tLoss: -12454.142187\n",
            "Train Epoch: 10 [30720/100000 (31%)]\tLoss: -12215.317188\n",
            "Train Epoch: 10 [33280/100000 (33%)]\tLoss: -13209.300000\n",
            "Train Epoch: 10 [35840/100000 (36%)]\tLoss: -13206.242188\n",
            "Train Epoch: 10 [38400/100000 (38%)]\tLoss: -13051.058594\n",
            "Train Epoch: 10 [40960/100000 (41%)]\tLoss: -11945.192188\n",
            "Train Epoch: 10 [43520/100000 (43%)]\tLoss: -13132.542187\n",
            "Train Epoch: 10 [46080/100000 (46%)]\tLoss: -13171.120312\n",
            "Train Epoch: 10 [48640/100000 (49%)]\tLoss: -12664.356250\n",
            "Train Epoch: 10 [51200/100000 (51%)]\tLoss: -14120.598438\n",
            "Train Epoch: 10 [53760/100000 (54%)]\tLoss: -13272.779688\n",
            "Train Epoch: 10 [56320/100000 (56%)]\tLoss: -12701.191406\n",
            "Train Epoch: 10 [58880/100000 (59%)]\tLoss: -12332.925781\n",
            "Train Epoch: 10 [61440/100000 (61%)]\tLoss: -13384.801563\n",
            "Train Epoch: 10 [64000/100000 (64%)]\tLoss: -12933.351562\n",
            "Train Epoch: 10 [66560/100000 (66%)]\tLoss: -12955.546875\n",
            "Train Epoch: 10 [69120/100000 (69%)]\tLoss: -12994.578906\n",
            "Train Epoch: 10 [71680/100000 (72%)]\tLoss: -14064.914062\n",
            "Train Epoch: 10 [74240/100000 (74%)]\tLoss: -13510.314062\n",
            "Train Epoch: 10 [76800/100000 (77%)]\tLoss: -13174.598438\n",
            "Train Epoch: 10 [79360/100000 (79%)]\tLoss: -13279.762500\n",
            "Train Epoch: 10 [81920/100000 (82%)]\tLoss: -13308.062500\n",
            "Train Epoch: 10 [84480/100000 (84%)]\tLoss: -12466.578906\n",
            "Train Epoch: 10 [87040/100000 (87%)]\tLoss: -12811.256250\n",
            "Train Epoch: 10 [89600/100000 (90%)]\tLoss: -13168.389063\n",
            "Train Epoch: 10 [92160/100000 (92%)]\tLoss: -13398.934375\n",
            "Train Epoch: 10 [94720/100000 (95%)]\tLoss: -12775.660156\n",
            "Train Epoch: 10 [97280/100000 (97%)]\tLoss: -13675.348438\n",
            "Train Epoch: 10 [62400/100000 (100%)]\tLoss: -8645.706981\n",
            "====> Epoch: 10 Average loss: -131012.7501\n",
            "Train Epoch: 11 [0/100000 (0%)]\tLoss: -126995.843750\n",
            "Train Epoch: 11 [2560/100000 (3%)]\tLoss: -12708.458594\n",
            "Train Epoch: 11 [5120/100000 (5%)]\tLoss: -13358.006250\n",
            "Train Epoch: 11 [7680/100000 (8%)]\tLoss: -13318.212500\n",
            "Train Epoch: 11 [10240/100000 (10%)]\tLoss: -12767.072656\n",
            "Train Epoch: 11 [12800/100000 (13%)]\tLoss: -12521.096875\n",
            "Train Epoch: 11 [15360/100000 (15%)]\tLoss: -13118.048437\n",
            "Train Epoch: 11 [17920/100000 (18%)]\tLoss: -13009.800000\n",
            "Train Epoch: 11 [20480/100000 (20%)]\tLoss: -13283.539062\n",
            "Train Epoch: 11 [23040/100000 (23%)]\tLoss: -13088.567969\n",
            "Train Epoch: 11 [25600/100000 (26%)]\tLoss: -13408.057812\n",
            "Train Epoch: 11 [28160/100000 (28%)]\tLoss: -14797.228125\n",
            "Train Epoch: 11 [30720/100000 (31%)]\tLoss: -12404.542969\n",
            "Train Epoch: 11 [33280/100000 (33%)]\tLoss: -13357.839062\n",
            "Train Epoch: 11 [35840/100000 (36%)]\tLoss: -13121.431250\n",
            "Train Epoch: 11 [38400/100000 (38%)]\tLoss: -12630.596094\n",
            "Train Epoch: 11 [40960/100000 (41%)]\tLoss: -13215.990625\n",
            "Train Epoch: 11 [43520/100000 (43%)]\tLoss: -12883.004688\n",
            "Train Epoch: 11 [46080/100000 (46%)]\tLoss: -12630.410156\n",
            "Train Epoch: 11 [48640/100000 (49%)]\tLoss: -13340.140625\n",
            "Train Epoch: 11 [51200/100000 (51%)]\tLoss: -12912.725000\n",
            "Train Epoch: 11 [53760/100000 (54%)]\tLoss: -12997.969531\n",
            "Train Epoch: 11 [56320/100000 (56%)]\tLoss: -13212.114063\n",
            "Train Epoch: 11 [58880/100000 (59%)]\tLoss: -12435.770313\n",
            "Train Epoch: 11 [61440/100000 (61%)]\tLoss: -13414.203125\n",
            "Train Epoch: 11 [64000/100000 (64%)]\tLoss: -13336.439062\n",
            "Train Epoch: 11 [66560/100000 (66%)]\tLoss: -14064.181250\n",
            "Train Epoch: 11 [69120/100000 (69%)]\tLoss: -12349.541406\n",
            "Train Epoch: 11 [71680/100000 (72%)]\tLoss: -12979.685938\n",
            "Train Epoch: 11 [74240/100000 (74%)]\tLoss: -13616.518750\n",
            "Train Epoch: 11 [76800/100000 (77%)]\tLoss: -12239.976562\n",
            "Train Epoch: 11 [79360/100000 (79%)]\tLoss: -13228.515625\n",
            "Train Epoch: 11 [81920/100000 (82%)]\tLoss: -12800.064844\n",
            "Train Epoch: 11 [84480/100000 (84%)]\tLoss: -12947.885156\n",
            "Train Epoch: 11 [87040/100000 (87%)]\tLoss: -12803.255469\n",
            "Train Epoch: 11 [89600/100000 (90%)]\tLoss: -12798.521875\n",
            "Train Epoch: 11 [92160/100000 (92%)]\tLoss: -12793.481250\n",
            "Train Epoch: 11 [94720/100000 (95%)]\tLoss: -12048.394531\n",
            "Train Epoch: 11 [97280/100000 (97%)]\tLoss: -13065.211719\n",
            "Train Epoch: 11 [62400/100000 (100%)]\tLoss: -8689.983766\n",
            "====> Epoch: 11 Average loss: -130166.1068\n",
            "Train Epoch: 12 [0/100000 (0%)]\tLoss: -130063.148438\n",
            "Train Epoch: 12 [2560/100000 (3%)]\tLoss: -12565.174219\n",
            "Train Epoch: 12 [5120/100000 (5%)]\tLoss: -13484.987500\n",
            "Train Epoch: 12 [7680/100000 (8%)]\tLoss: -12433.340625\n",
            "Train Epoch: 12 [10240/100000 (10%)]\tLoss: -13066.763281\n",
            "Train Epoch: 12 [12800/100000 (13%)]\tLoss: -13040.909375\n",
            "Train Epoch: 12 [15360/100000 (15%)]\tLoss: -13392.414062\n",
            "Train Epoch: 12 [17920/100000 (18%)]\tLoss: -11903.360156\n",
            "Train Epoch: 12 [20480/100000 (20%)]\tLoss: -13312.925000\n",
            "Train Epoch: 12 [23040/100000 (23%)]\tLoss: -12189.689062\n",
            "Train Epoch: 12 [25600/100000 (26%)]\tLoss: -12847.447656\n",
            "Train Epoch: 12 [28160/100000 (28%)]\tLoss: -13218.304688\n",
            "Train Epoch: 12 [30720/100000 (31%)]\tLoss: -13039.440625\n",
            "Train Epoch: 12 [33280/100000 (33%)]\tLoss: -13287.128125\n",
            "Train Epoch: 12 [35840/100000 (36%)]\tLoss: -12813.475781\n",
            "Train Epoch: 12 [38400/100000 (38%)]\tLoss: -12334.106250\n",
            "Train Epoch: 12 [40960/100000 (41%)]\tLoss: -13180.639063\n",
            "Train Epoch: 12 [43520/100000 (43%)]\tLoss: -12933.396875\n",
            "Train Epoch: 12 [46080/100000 (46%)]\tLoss: -12362.213281\n",
            "Train Epoch: 12 [48640/100000 (49%)]\tLoss: -13394.031250\n",
            "Train Epoch: 12 [51200/100000 (51%)]\tLoss: -13908.162500\n",
            "Train Epoch: 12 [53760/100000 (54%)]\tLoss: -13969.173437\n",
            "Train Epoch: 12 [56320/100000 (56%)]\tLoss: -13284.642187\n",
            "Train Epoch: 12 [58880/100000 (59%)]\tLoss: -12836.853906\n",
            "Train Epoch: 12 [61440/100000 (61%)]\tLoss: -13216.365625\n",
            "Train Epoch: 12 [64000/100000 (64%)]\tLoss: -12996.159375\n",
            "Train Epoch: 12 [66560/100000 (66%)]\tLoss: -13608.846875\n",
            "Train Epoch: 12 [69120/100000 (69%)]\tLoss: -12410.741406\n",
            "Train Epoch: 12 [71680/100000 (72%)]\tLoss: -12056.592969\n",
            "Train Epoch: 12 [74240/100000 (74%)]\tLoss: -13745.456250\n",
            "Train Epoch: 12 [76800/100000 (77%)]\tLoss: -13520.501562\n",
            "Train Epoch: 12 [79360/100000 (79%)]\tLoss: -13526.309375\n",
            "Train Epoch: 12 [81920/100000 (82%)]\tLoss: -12684.370312\n",
            "Train Epoch: 12 [84480/100000 (84%)]\tLoss: -13083.490625\n",
            "Train Epoch: 12 [87040/100000 (87%)]\tLoss: -12813.538281\n",
            "Train Epoch: 12 [89600/100000 (90%)]\tLoss: -12199.125781\n",
            "Train Epoch: 12 [92160/100000 (92%)]\tLoss: -13230.389063\n",
            "Train Epoch: 12 [94720/100000 (95%)]\tLoss: -13282.971875\n",
            "Train Epoch: 12 [97280/100000 (97%)]\tLoss: -13275.815625\n",
            "Train Epoch: 12 [62400/100000 (100%)]\tLoss: -7972.815747\n",
            "====> Epoch: 12 Average loss: -129584.2522\n",
            "Train Epoch: 13 [0/100000 (0%)]\tLoss: -123496.656250\n",
            "Train Epoch: 13 [2560/100000 (3%)]\tLoss: -13206.525000\n",
            "Train Epoch: 13 [5120/100000 (5%)]\tLoss: -13371.435938\n",
            "Train Epoch: 13 [7680/100000 (8%)]\tLoss: -12665.621094\n",
            "Train Epoch: 13 [10240/100000 (10%)]\tLoss: -13587.634375\n",
            "Train Epoch: 13 [12800/100000 (13%)]\tLoss: -13093.600000\n",
            "Train Epoch: 13 [15360/100000 (15%)]\tLoss: -12750.240625\n",
            "Train Epoch: 13 [17920/100000 (18%)]\tLoss: -13119.107813\n",
            "Train Epoch: 13 [20480/100000 (20%)]\tLoss: -12945.696094\n",
            "Train Epoch: 13 [23040/100000 (23%)]\tLoss: -13459.418750\n",
            "Train Epoch: 13 [25600/100000 (26%)]\tLoss: -13058.898438\n",
            "Train Epoch: 13 [28160/100000 (28%)]\tLoss: -12597.814844\n",
            "Train Epoch: 13 [30720/100000 (31%)]\tLoss: -12535.495312\n",
            "Train Epoch: 13 [33280/100000 (33%)]\tLoss: -12413.849219\n",
            "Train Epoch: 13 [35840/100000 (36%)]\tLoss: -12543.345312\n",
            "Train Epoch: 13 [38400/100000 (38%)]\tLoss: -13540.682812\n",
            "Train Epoch: 13 [40960/100000 (41%)]\tLoss: -12360.395313\n",
            "Train Epoch: 13 [43520/100000 (43%)]\tLoss: -12987.292969\n",
            "Train Epoch: 13 [46080/100000 (46%)]\tLoss: -12701.510937\n",
            "Train Epoch: 13 [48640/100000 (49%)]\tLoss: -13210.101562\n",
            "Train Epoch: 13 [51200/100000 (51%)]\tLoss: -13242.303125\n",
            "Train Epoch: 13 [53760/100000 (54%)]\tLoss: -12757.332031\n",
            "Train Epoch: 13 [56320/100000 (56%)]\tLoss: -13460.307812\n",
            "Train Epoch: 13 [58880/100000 (59%)]\tLoss: -13381.503125\n",
            "Train Epoch: 13 [61440/100000 (61%)]\tLoss: -12445.582031\n",
            "Train Epoch: 13 [64000/100000 (64%)]\tLoss: -12913.580469\n",
            "Train Epoch: 13 [66560/100000 (66%)]\tLoss: -12991.357813\n",
            "Train Epoch: 13 [69120/100000 (69%)]\tLoss: -13471.937500\n",
            "Train Epoch: 13 [71680/100000 (72%)]\tLoss: -12718.981250\n",
            "Train Epoch: 13 [74240/100000 (74%)]\tLoss: -12407.300781\n",
            "Train Epoch: 13 [76800/100000 (77%)]\tLoss: -13100.673437\n",
            "Train Epoch: 13 [79360/100000 (79%)]\tLoss: -12370.322656\n",
            "Train Epoch: 13 [81920/100000 (82%)]\tLoss: -13165.856250\n",
            "Train Epoch: 13 [84480/100000 (84%)]\tLoss: -12929.617188\n",
            "Train Epoch: 13 [87040/100000 (87%)]\tLoss: -12887.425000\n",
            "Train Epoch: 13 [89600/100000 (90%)]\tLoss: -12180.695312\n",
            "Train Epoch: 13 [92160/100000 (92%)]\tLoss: -12784.272656\n",
            "Train Epoch: 13 [94720/100000 (95%)]\tLoss: -13009.713281\n",
            "Train Epoch: 13 [97280/100000 (97%)]\tLoss: -12777.751562\n",
            "Train Epoch: 13 [62400/100000 (100%)]\tLoss: -8806.562500\n",
            "====> Epoch: 13 Average loss: -128844.0530\n",
            "Train Epoch: 14 [0/100000 (0%)]\tLoss: -130611.609375\n",
            "Train Epoch: 14 [2560/100000 (3%)]\tLoss: -12952.864063\n",
            "Train Epoch: 14 [5120/100000 (5%)]\tLoss: -13471.368750\n",
            "Train Epoch: 14 [7680/100000 (8%)]\tLoss: -12836.992188\n",
            "Train Epoch: 14 [10240/100000 (10%)]\tLoss: -12658.247656\n",
            "Train Epoch: 14 [12800/100000 (13%)]\tLoss: -13181.951563\n",
            "Train Epoch: 14 [15360/100000 (15%)]\tLoss: -12239.330469\n",
            "Train Epoch: 14 [17920/100000 (18%)]\tLoss: -13200.073437\n",
            "Train Epoch: 14 [20480/100000 (20%)]\tLoss: -12748.638281\n",
            "Train Epoch: 14 [23040/100000 (23%)]\tLoss: -13140.539062\n",
            "Train Epoch: 14 [25600/100000 (26%)]\tLoss: -13210.276562\n",
            "Train Epoch: 14 [28160/100000 (28%)]\tLoss: -12467.561719\n",
            "Train Epoch: 14 [30720/100000 (31%)]\tLoss: -13404.485937\n",
            "Train Epoch: 14 [33280/100000 (33%)]\tLoss: -13783.050000\n",
            "Train Epoch: 14 [35840/100000 (36%)]\tLoss: -13349.421875\n",
            "Train Epoch: 14 [38400/100000 (38%)]\tLoss: -13325.057812\n",
            "Train Epoch: 14 [40960/100000 (41%)]\tLoss: -12813.367188\n",
            "Train Epoch: 14 [43520/100000 (43%)]\tLoss: -12920.971094\n",
            "Train Epoch: 14 [46080/100000 (46%)]\tLoss: -12829.292187\n",
            "Train Epoch: 14 [48640/100000 (49%)]\tLoss: -12718.796875\n",
            "Train Epoch: 14 [51200/100000 (51%)]\tLoss: -12935.828125\n",
            "Train Epoch: 14 [53760/100000 (54%)]\tLoss: -11972.894531\n",
            "Train Epoch: 14 [56320/100000 (56%)]\tLoss: -13033.698437\n",
            "Train Epoch: 14 [58880/100000 (59%)]\tLoss: -12588.135156\n",
            "Train Epoch: 14 [61440/100000 (61%)]\tLoss: -12971.025781\n",
            "Train Epoch: 14 [64000/100000 (64%)]\tLoss: -12513.278125\n",
            "Train Epoch: 14 [66560/100000 (66%)]\tLoss: -12831.975781\n",
            "Train Epoch: 14 [69120/100000 (69%)]\tLoss: -12126.030469\n",
            "Train Epoch: 14 [71680/100000 (72%)]\tLoss: -12481.046094\n",
            "Train Epoch: 14 [74240/100000 (74%)]\tLoss: -13211.529688\n",
            "Train Epoch: 14 [76800/100000 (77%)]\tLoss: -12847.290625\n",
            "Train Epoch: 14 [79360/100000 (79%)]\tLoss: -12550.134375\n",
            "Train Epoch: 14 [81920/100000 (82%)]\tLoss: -13050.871875\n",
            "Train Epoch: 14 [84480/100000 (84%)]\tLoss: -12658.310938\n",
            "Train Epoch: 14 [87040/100000 (87%)]\tLoss: -13613.653125\n",
            "Train Epoch: 14 [89600/100000 (90%)]\tLoss: -13014.251562\n",
            "Train Epoch: 14 [92160/100000 (92%)]\tLoss: -13096.167187\n",
            "Train Epoch: 14 [94720/100000 (95%)]\tLoss: -12573.703125\n",
            "Train Epoch: 14 [97280/100000 (97%)]\tLoss: -13134.631250\n",
            "Train Epoch: 14 [62400/100000 (100%)]\tLoss: -8260.040584\n",
            "====> Epoch: 14 Average loss: -128105.0599\n",
            "Train Epoch: 15 [0/100000 (0%)]\tLoss: -127678.843750\n",
            "Train Epoch: 15 [2560/100000 (3%)]\tLoss: -13252.387500\n",
            "Train Epoch: 15 [5120/100000 (5%)]\tLoss: -13299.351562\n",
            "Train Epoch: 15 [7680/100000 (8%)]\tLoss: -12438.174219\n",
            "Train Epoch: 15 [10240/100000 (10%)]\tLoss: -12430.175781\n",
            "Train Epoch: 15 [12800/100000 (13%)]\tLoss: -12233.845312\n",
            "Train Epoch: 15 [15360/100000 (15%)]\tLoss: -12858.899219\n",
            "Train Epoch: 15 [17920/100000 (18%)]\tLoss: -12167.165625\n",
            "Train Epoch: 15 [20480/100000 (20%)]\tLoss: -12589.360937\n",
            "Train Epoch: 15 [23040/100000 (23%)]\tLoss: -12951.638281\n",
            "Train Epoch: 15 [25600/100000 (26%)]\tLoss: -12759.438281\n",
            "Train Epoch: 15 [28160/100000 (28%)]\tLoss: -12582.713281\n",
            "Train Epoch: 15 [30720/100000 (31%)]\tLoss: -12831.095312\n",
            "Train Epoch: 15 [33280/100000 (33%)]\tLoss: -12980.360937\n",
            "Train Epoch: 15 [35840/100000 (36%)]\tLoss: -11952.457813\n",
            "Train Epoch: 15 [38400/100000 (38%)]\tLoss: -12242.933594\n",
            "Train Epoch: 15 [40960/100000 (41%)]\tLoss: -13254.353125\n",
            "Train Epoch: 15 [43520/100000 (43%)]\tLoss: -13015.989063\n",
            "Train Epoch: 15 [46080/100000 (46%)]\tLoss: -12095.592188\n",
            "Train Epoch: 15 [48640/100000 (49%)]\tLoss: -12715.894531\n",
            "Train Epoch: 15 [51200/100000 (51%)]\tLoss: -12611.635937\n",
            "Train Epoch: 15 [53760/100000 (54%)]\tLoss: -13136.467188\n",
            "Train Epoch: 15 [56320/100000 (56%)]\tLoss: -12947.801563\n",
            "Train Epoch: 15 [58880/100000 (59%)]\tLoss: -11964.889844\n",
            "Train Epoch: 15 [61440/100000 (61%)]\tLoss: -12348.836719\n",
            "Train Epoch: 15 [64000/100000 (64%)]\tLoss: -13058.034375\n",
            "Train Epoch: 15 [66560/100000 (66%)]\tLoss: -12879.039844\n",
            "Train Epoch: 15 [69120/100000 (69%)]\tLoss: -12679.832813\n",
            "Train Epoch: 15 [71680/100000 (72%)]\tLoss: -12422.436719\n",
            "Train Epoch: 15 [74240/100000 (74%)]\tLoss: -12526.349219\n",
            "Train Epoch: 15 [76800/100000 (77%)]\tLoss: -13208.382812\n",
            "Train Epoch: 15 [79360/100000 (79%)]\tLoss: -13202.585938\n",
            "Train Epoch: 15 [81920/100000 (82%)]\tLoss: -12708.296094\n",
            "Train Epoch: 15 [84480/100000 (84%)]\tLoss: -12015.050781\n",
            "Train Epoch: 15 [87040/100000 (87%)]\tLoss: -13031.980469\n",
            "Train Epoch: 15 [89600/100000 (90%)]\tLoss: -13277.887500\n",
            "Train Epoch: 15 [92160/100000 (92%)]\tLoss: -12250.596094\n",
            "Train Epoch: 15 [94720/100000 (95%)]\tLoss: -11753.287500\n",
            "Train Epoch: 15 [97280/100000 (97%)]\tLoss: -12556.551563\n",
            "Train Epoch: 15 [62400/100000 (100%)]\tLoss: -8031.650974\n",
            "====> Epoch: 15 Average loss: -127490.6913\n",
            "Train Epoch: 16 [0/100000 (0%)]\tLoss: -131149.812500\n",
            "Train Epoch: 16 [2560/100000 (3%)]\tLoss: -12958.951563\n",
            "Train Epoch: 16 [5120/100000 (5%)]\tLoss: -12627.484375\n",
            "Train Epoch: 16 [7680/100000 (8%)]\tLoss: -12616.175781\n",
            "Train Epoch: 16 [10240/100000 (10%)]\tLoss: -12254.467188\n",
            "Train Epoch: 16 [12800/100000 (13%)]\tLoss: -12884.541406\n",
            "Train Epoch: 16 [15360/100000 (15%)]\tLoss: -12694.035938\n",
            "Train Epoch: 16 [17920/100000 (18%)]\tLoss: -12913.510156\n",
            "Train Epoch: 16 [20480/100000 (20%)]\tLoss: -12133.239063\n",
            "Train Epoch: 16 [23040/100000 (23%)]\tLoss: -12907.947656\n",
            "Train Epoch: 16 [25600/100000 (26%)]\tLoss: -11843.675781\n",
            "Train Epoch: 16 [28160/100000 (28%)]\tLoss: -12744.247656\n",
            "Train Epoch: 16 [30720/100000 (31%)]\tLoss: -12407.201563\n",
            "Train Epoch: 16 [33280/100000 (33%)]\tLoss: -12440.936719\n",
            "Train Epoch: 16 [35840/100000 (36%)]\tLoss: -12212.460156\n",
            "Train Epoch: 16 [38400/100000 (38%)]\tLoss: -13131.378125\n",
            "Train Epoch: 16 [40960/100000 (41%)]\tLoss: -12736.807031\n",
            "Train Epoch: 16 [43520/100000 (43%)]\tLoss: -13230.656250\n",
            "Train Epoch: 16 [46080/100000 (46%)]\tLoss: -12711.894531\n",
            "Train Epoch: 16 [48640/100000 (49%)]\tLoss: -12873.287500\n",
            "Train Epoch: 16 [51200/100000 (51%)]\tLoss: -12423.602344\n",
            "Train Epoch: 16 [53760/100000 (54%)]\tLoss: -12002.852344\n",
            "Train Epoch: 16 [56320/100000 (56%)]\tLoss: -12191.215625\n",
            "Train Epoch: 16 [58880/100000 (59%)]\tLoss: -12834.080469\n",
            "Train Epoch: 16 [61440/100000 (61%)]\tLoss: -12095.110156\n",
            "Train Epoch: 16 [64000/100000 (64%)]\tLoss: -12921.650781\n",
            "Train Epoch: 16 [66560/100000 (66%)]\tLoss: -13015.929688\n",
            "Train Epoch: 16 [69120/100000 (69%)]\tLoss: -13057.257812\n",
            "Train Epoch: 16 [71680/100000 (72%)]\tLoss: -12762.421875\n",
            "Train Epoch: 16 [74240/100000 (74%)]\tLoss: -13002.039062\n",
            "Train Epoch: 16 [76800/100000 (77%)]\tLoss: -12780.078125\n",
            "Train Epoch: 16 [79360/100000 (79%)]\tLoss: -12634.102344\n",
            "Train Epoch: 16 [81920/100000 (82%)]\tLoss: -12788.650000\n",
            "Train Epoch: 16 [84480/100000 (84%)]\tLoss: -12879.974219\n",
            "Train Epoch: 16 [87040/100000 (87%)]\tLoss: -12411.101562\n",
            "Train Epoch: 16 [89600/100000 (90%)]\tLoss: -12445.123438\n",
            "Train Epoch: 16 [92160/100000 (92%)]\tLoss: -13364.403125\n",
            "Train Epoch: 16 [94720/100000 (95%)]\tLoss: -13192.373438\n",
            "Train Epoch: 16 [97280/100000 (97%)]\tLoss: -12221.493750\n",
            "Train Epoch: 16 [62400/100000 (100%)]\tLoss: -8405.156656\n",
            "====> Epoch: 16 Average loss: -127161.3582\n",
            "Train Epoch: 17 [0/100000 (0%)]\tLoss: -121943.578125\n",
            "Train Epoch: 17 [2560/100000 (3%)]\tLoss: -12823.938281\n",
            "Train Epoch: 17 [5120/100000 (5%)]\tLoss: -13156.654688\n",
            "Train Epoch: 17 [7680/100000 (8%)]\tLoss: -12391.264844\n",
            "Train Epoch: 17 [10240/100000 (10%)]\tLoss: -13360.810938\n",
            "Train Epoch: 17 [12800/100000 (13%)]\tLoss: -13061.564844\n",
            "Train Epoch: 17 [15360/100000 (15%)]\tLoss: -12358.634375\n",
            "Train Epoch: 17 [17920/100000 (18%)]\tLoss: -12773.670313\n",
            "Train Epoch: 17 [20480/100000 (20%)]\tLoss: -13004.639063\n",
            "Train Epoch: 17 [23040/100000 (23%)]\tLoss: -12557.100000\n",
            "Train Epoch: 17 [25600/100000 (26%)]\tLoss: -13209.582813\n",
            "Train Epoch: 17 [28160/100000 (28%)]\tLoss: -12860.575781\n",
            "Train Epoch: 17 [30720/100000 (31%)]\tLoss: -12448.937500\n",
            "Train Epoch: 17 [33280/100000 (33%)]\tLoss: -13188.078125\n",
            "Train Epoch: 17 [35840/100000 (36%)]\tLoss: -12470.447656\n",
            "Train Epoch: 17 [38400/100000 (38%)]\tLoss: -13528.323437\n",
            "Train Epoch: 17 [40960/100000 (41%)]\tLoss: -12295.146094\n",
            "Train Epoch: 17 [43520/100000 (43%)]\tLoss: -12002.372656\n",
            "Train Epoch: 17 [46080/100000 (46%)]\tLoss: -12676.232813\n",
            "Train Epoch: 17 [48640/100000 (49%)]\tLoss: -12087.205469\n",
            "Train Epoch: 17 [51200/100000 (51%)]\tLoss: -12848.175000\n",
            "Train Epoch: 17 [53760/100000 (54%)]\tLoss: -12981.394531\n",
            "Train Epoch: 17 [56320/100000 (56%)]\tLoss: -13388.831250\n",
            "Train Epoch: 17 [58880/100000 (59%)]\tLoss: -13155.176563\n",
            "Train Epoch: 17 [61440/100000 (61%)]\tLoss: -13239.862500\n",
            "Train Epoch: 17 [64000/100000 (64%)]\tLoss: -11877.407031\n",
            "Train Epoch: 17 [66560/100000 (66%)]\tLoss: -12394.835938\n",
            "Train Epoch: 17 [69120/100000 (69%)]\tLoss: -12986.779688\n",
            "Train Epoch: 17 [71680/100000 (72%)]\tLoss: -12761.232031\n",
            "Train Epoch: 17 [74240/100000 (74%)]\tLoss: -12945.662500\n",
            "Train Epoch: 17 [76800/100000 (77%)]\tLoss: -13039.755469\n",
            "Train Epoch: 17 [79360/100000 (79%)]\tLoss: -12560.960938\n",
            "Train Epoch: 17 [81920/100000 (82%)]\tLoss: -11606.661719\n",
            "Train Epoch: 17 [84480/100000 (84%)]\tLoss: -12930.428125\n",
            "Train Epoch: 17 [87040/100000 (87%)]\tLoss: -11849.635937\n",
            "Train Epoch: 17 [89600/100000 (90%)]\tLoss: -12403.367188\n",
            "Train Epoch: 17 [92160/100000 (92%)]\tLoss: -13293.012500\n",
            "Train Epoch: 17 [94720/100000 (95%)]\tLoss: -12830.136719\n",
            "Train Epoch: 17 [97280/100000 (97%)]\tLoss: -12494.706250\n",
            "Train Epoch: 17 [62400/100000 (100%)]\tLoss: -7792.711851\n",
            "====> Epoch: 17 Average loss: -126781.2696\n",
            "Train Epoch: 18 [0/100000 (0%)]\tLoss: -126849.304688\n",
            "Train Epoch: 18 [2560/100000 (3%)]\tLoss: -13315.332813\n",
            "Train Epoch: 18 [5120/100000 (5%)]\tLoss: -12876.780469\n",
            "Train Epoch: 18 [7680/100000 (8%)]\tLoss: -12362.521094\n",
            "Train Epoch: 18 [10240/100000 (10%)]\tLoss: -12785.846875\n",
            "Train Epoch: 18 [12800/100000 (13%)]\tLoss: -12938.271094\n",
            "Train Epoch: 18 [15360/100000 (15%)]\tLoss: -12521.107031\n",
            "Train Epoch: 18 [17920/100000 (18%)]\tLoss: -12492.510937\n",
            "Train Epoch: 18 [20480/100000 (20%)]\tLoss: -12664.096094\n",
            "Train Epoch: 18 [23040/100000 (23%)]\tLoss: -12849.381250\n",
            "Train Epoch: 18 [25600/100000 (26%)]\tLoss: -12385.070312\n",
            "Train Epoch: 18 [28160/100000 (28%)]\tLoss: -12324.085156\n",
            "Train Epoch: 18 [30720/100000 (31%)]\tLoss: -12835.294531\n",
            "Train Epoch: 18 [33280/100000 (33%)]\tLoss: -12649.076563\n",
            "Train Epoch: 18 [35840/100000 (36%)]\tLoss: -12949.117188\n",
            "Train Epoch: 18 [38400/100000 (38%)]\tLoss: -12345.049219\n",
            "Train Epoch: 18 [40960/100000 (41%)]\tLoss: -13135.839062\n",
            "Train Epoch: 18 [43520/100000 (43%)]\tLoss: -12994.085156\n",
            "Train Epoch: 18 [46080/100000 (46%)]\tLoss: -12936.898438\n",
            "Train Epoch: 18 [48640/100000 (49%)]\tLoss: -11968.307031\n",
            "Train Epoch: 18 [51200/100000 (51%)]\tLoss: -11953.793750\n",
            "Train Epoch: 18 [53760/100000 (54%)]\tLoss: -12245.089062\n",
            "Train Epoch: 18 [56320/100000 (56%)]\tLoss: -13229.335938\n",
            "Train Epoch: 18 [58880/100000 (59%)]\tLoss: -12950.270313\n",
            "Train Epoch: 18 [61440/100000 (61%)]\tLoss: -12022.125000\n",
            "Train Epoch: 18 [64000/100000 (64%)]\tLoss: -12835.666406\n",
            "Train Epoch: 18 [66560/100000 (66%)]\tLoss: -12928.714844\n",
            "Train Epoch: 18 [69120/100000 (69%)]\tLoss: -12539.875000\n",
            "Train Epoch: 18 [71680/100000 (72%)]\tLoss: -13015.187500\n",
            "Train Epoch: 18 [74240/100000 (74%)]\tLoss: -12667.857813\n",
            "Train Epoch: 18 [76800/100000 (77%)]\tLoss: -12551.298437\n",
            "Train Epoch: 18 [79360/100000 (79%)]\tLoss: -12860.828906\n",
            "Train Epoch: 18 [81920/100000 (82%)]\tLoss: -11995.925781\n",
            "Train Epoch: 18 [84480/100000 (84%)]\tLoss: -12917.797656\n",
            "Train Epoch: 18 [87040/100000 (87%)]\tLoss: -12287.682812\n",
            "Train Epoch: 18 [89600/100000 (90%)]\tLoss: -13038.836719\n",
            "Train Epoch: 18 [92160/100000 (92%)]\tLoss: -13335.948437\n",
            "Train Epoch: 18 [94720/100000 (95%)]\tLoss: -11745.960938\n",
            "Train Epoch: 18 [97280/100000 (97%)]\tLoss: -11948.477344\n",
            "Train Epoch: 18 [62400/100000 (100%)]\tLoss: -8319.129058\n",
            "====> Epoch: 18 Average loss: -126245.3504\n",
            "Train Epoch: 19 [0/100000 (0%)]\tLoss: -122289.351562\n",
            "Train Epoch: 19 [2560/100000 (3%)]\tLoss: -13085.547656\n",
            "Train Epoch: 19 [5120/100000 (5%)]\tLoss: -12791.134375\n",
            "Train Epoch: 19 [7680/100000 (8%)]\tLoss: -12455.329687\n",
            "Train Epoch: 19 [10240/100000 (10%)]\tLoss: -12897.644531\n",
            "Train Epoch: 19 [12800/100000 (13%)]\tLoss: -12689.522656\n",
            "Train Epoch: 19 [15360/100000 (15%)]\tLoss: -12562.650781\n",
            "Train Epoch: 19 [17920/100000 (18%)]\tLoss: -12157.782031\n",
            "Train Epoch: 19 [20480/100000 (20%)]\tLoss: -12487.482031\n",
            "Train Epoch: 19 [23040/100000 (23%)]\tLoss: -12347.487500\n",
            "Train Epoch: 19 [25600/100000 (26%)]\tLoss: -13405.454687\n",
            "Train Epoch: 19 [28160/100000 (28%)]\tLoss: -12627.172656\n",
            "Train Epoch: 19 [30720/100000 (31%)]\tLoss: -12293.830469\n",
            "Train Epoch: 19 [33280/100000 (33%)]\tLoss: -11608.463281\n",
            "Train Epoch: 19 [35840/100000 (36%)]\tLoss: -12842.862500\n",
            "Train Epoch: 19 [38400/100000 (38%)]\tLoss: -12494.214844\n",
            "Train Epoch: 19 [40960/100000 (41%)]\tLoss: -13091.625781\n",
            "Train Epoch: 19 [43520/100000 (43%)]\tLoss: -12514.497656\n",
            "Train Epoch: 19 [46080/100000 (46%)]\tLoss: -12435.133594\n",
            "Train Epoch: 19 [48640/100000 (49%)]\tLoss: -12158.932812\n",
            "Train Epoch: 19 [51200/100000 (51%)]\tLoss: -12014.270313\n",
            "Train Epoch: 19 [53760/100000 (54%)]\tLoss: -12152.366406\n",
            "Train Epoch: 19 [56320/100000 (56%)]\tLoss: -12025.881250\n",
            "Train Epoch: 19 [58880/100000 (59%)]\tLoss: -11872.609375\n",
            "Train Epoch: 19 [61440/100000 (61%)]\tLoss: -12557.621094\n",
            "Train Epoch: 19 [64000/100000 (64%)]\tLoss: -13346.526562\n",
            "Train Epoch: 19 [66560/100000 (66%)]\tLoss: -12552.956250\n",
            "Train Epoch: 19 [69120/100000 (69%)]\tLoss: -12801.296875\n",
            "Train Epoch: 19 [71680/100000 (72%)]\tLoss: -12811.745312\n",
            "Train Epoch: 19 [74240/100000 (74%)]\tLoss: -12635.043750\n",
            "Train Epoch: 19 [76800/100000 (77%)]\tLoss: -13091.409375\n",
            "Train Epoch: 19 [79360/100000 (79%)]\tLoss: -12065.318750\n",
            "Train Epoch: 19 [81920/100000 (82%)]\tLoss: -12557.638281\n",
            "Train Epoch: 19 [84480/100000 (84%)]\tLoss: -13133.512500\n",
            "Train Epoch: 19 [87040/100000 (87%)]\tLoss: -12762.403906\n",
            "Train Epoch: 19 [89600/100000 (90%)]\tLoss: -12699.489063\n",
            "Train Epoch: 19 [92160/100000 (92%)]\tLoss: -12359.800781\n",
            "Train Epoch: 19 [94720/100000 (95%)]\tLoss: -12979.482813\n",
            "Train Epoch: 19 [97280/100000 (97%)]\tLoss: -12264.360156\n",
            "Train Epoch: 19 [62400/100000 (100%)]\tLoss: -8240.050325\n",
            "====> Epoch: 19 Average loss: -125824.7699\n",
            "Train Epoch: 20 [0/100000 (0%)]\tLoss: -114565.578125\n",
            "Train Epoch: 20 [2560/100000 (3%)]\tLoss: -12029.410938\n",
            "Train Epoch: 20 [5120/100000 (5%)]\tLoss: -12875.264063\n",
            "Train Epoch: 20 [7680/100000 (8%)]\tLoss: -12481.334375\n",
            "Train Epoch: 20 [10240/100000 (10%)]\tLoss: -12385.016406\n",
            "Train Epoch: 20 [12800/100000 (13%)]\tLoss: -12853.226562\n",
            "Train Epoch: 20 [15360/100000 (15%)]\tLoss: -12750.981250\n",
            "Train Epoch: 20 [17920/100000 (18%)]\tLoss: -12623.439844\n",
            "Train Epoch: 20 [20480/100000 (20%)]\tLoss: -13501.565625\n",
            "Train Epoch: 20 [23040/100000 (23%)]\tLoss: -12416.710938\n",
            "Train Epoch: 20 [25600/100000 (26%)]\tLoss: -12849.327344\n",
            "Train Epoch: 20 [28160/100000 (28%)]\tLoss: -12460.830469\n",
            "Train Epoch: 20 [30720/100000 (31%)]\tLoss: -12369.753906\n",
            "Train Epoch: 20 [33280/100000 (33%)]\tLoss: -12822.440625\n",
            "Train Epoch: 20 [35840/100000 (36%)]\tLoss: -12127.155469\n",
            "Train Epoch: 20 [38400/100000 (38%)]\tLoss: -12380.760937\n",
            "Train Epoch: 20 [40960/100000 (41%)]\tLoss: -13382.621875\n",
            "Train Epoch: 20 [43520/100000 (43%)]\tLoss: -12053.969531\n",
            "Train Epoch: 20 [46080/100000 (46%)]\tLoss: -12518.985937\n",
            "Train Epoch: 20 [48640/100000 (49%)]\tLoss: -12674.080469\n",
            "Train Epoch: 20 [51200/100000 (51%)]\tLoss: -12029.590625\n",
            "Train Epoch: 20 [53760/100000 (54%)]\tLoss: -12010.505469\n",
            "Train Epoch: 20 [56320/100000 (56%)]\tLoss: -12175.375781\n",
            "Train Epoch: 20 [58880/100000 (59%)]\tLoss: -12746.739844\n",
            "Train Epoch: 20 [61440/100000 (61%)]\tLoss: -12174.969531\n",
            "Train Epoch: 20 [64000/100000 (64%)]\tLoss: -11735.442188\n",
            "Train Epoch: 20 [66560/100000 (66%)]\tLoss: -13026.760937\n",
            "Train Epoch: 20 [69120/100000 (69%)]\tLoss: -12698.792187\n",
            "Train Epoch: 20 [71680/100000 (72%)]\tLoss: -11726.318750\n",
            "Train Epoch: 20 [74240/100000 (74%)]\tLoss: -12820.545313\n",
            "Train Epoch: 20 [76800/100000 (77%)]\tLoss: -12970.771094\n",
            "Train Epoch: 20 [79360/100000 (79%)]\tLoss: -12363.991406\n",
            "Train Epoch: 20 [81920/100000 (82%)]\tLoss: -12725.609375\n",
            "Train Epoch: 20 [84480/100000 (84%)]\tLoss: -12417.888281\n",
            "Train Epoch: 20 [87040/100000 (87%)]\tLoss: -12122.619531\n",
            "Train Epoch: 20 [89600/100000 (90%)]\tLoss: -12320.078125\n",
            "Train Epoch: 20 [92160/100000 (92%)]\tLoss: -12336.919531\n",
            "Train Epoch: 20 [94720/100000 (95%)]\tLoss: -13006.649219\n",
            "Train Epoch: 20 [97280/100000 (97%)]\tLoss: -13116.442188\n",
            "Train Epoch: 20 [62400/100000 (100%)]\tLoss: -7877.201299\n",
            "====> Epoch: 20 Average loss: -125390.8982\n",
            "Train Epoch: 21 [0/100000 (0%)]\tLoss: -131073.500000\n",
            "Train Epoch: 21 [2560/100000 (3%)]\tLoss: -12962.922656\n",
            "Train Epoch: 21 [5120/100000 (5%)]\tLoss: -13052.287500\n",
            "Train Epoch: 21 [7680/100000 (8%)]\tLoss: -12405.800000\n",
            "Train Epoch: 21 [10240/100000 (10%)]\tLoss: -12224.592969\n",
            "Train Epoch: 21 [12800/100000 (13%)]\tLoss: -12530.074219\n",
            "Train Epoch: 21 [15360/100000 (15%)]\tLoss: -12340.029688\n",
            "Train Epoch: 21 [17920/100000 (18%)]\tLoss: -12557.322656\n",
            "Train Epoch: 21 [20480/100000 (20%)]\tLoss: -11889.712500\n",
            "Train Epoch: 21 [23040/100000 (23%)]\tLoss: -12242.525781\n",
            "Train Epoch: 21 [25600/100000 (26%)]\tLoss: -11795.501562\n",
            "Train Epoch: 21 [28160/100000 (28%)]\tLoss: -12864.092188\n",
            "Train Epoch: 21 [30720/100000 (31%)]\tLoss: -12639.453125\n",
            "Train Epoch: 21 [33280/100000 (33%)]\tLoss: -12933.880469\n",
            "Train Epoch: 21 [35840/100000 (36%)]\tLoss: -12995.128125\n",
            "Train Epoch: 21 [38400/100000 (38%)]\tLoss: -12424.300000\n",
            "Train Epoch: 21 [40960/100000 (41%)]\tLoss: -12934.482813\n",
            "Train Epoch: 21 [43520/100000 (43%)]\tLoss: -12471.306250\n",
            "Train Epoch: 21 [46080/100000 (46%)]\tLoss: -12673.496094\n",
            "Train Epoch: 21 [48640/100000 (49%)]\tLoss: -11953.801563\n",
            "Train Epoch: 21 [51200/100000 (51%)]\tLoss: -12700.090625\n",
            "Train Epoch: 21 [53760/100000 (54%)]\tLoss: -12350.073437\n",
            "Train Epoch: 21 [56320/100000 (56%)]\tLoss: -12200.057031\n",
            "Train Epoch: 21 [58880/100000 (59%)]\tLoss: -11963.529688\n",
            "Train Epoch: 21 [61440/100000 (61%)]\tLoss: -12108.096094\n",
            "Train Epoch: 21 [64000/100000 (64%)]\tLoss: -11817.497656\n",
            "Train Epoch: 21 [66560/100000 (66%)]\tLoss: -12927.657031\n",
            "Train Epoch: 21 [69120/100000 (69%)]\tLoss: -12235.808594\n",
            "Train Epoch: 21 [71680/100000 (72%)]\tLoss: -12254.078906\n",
            "Train Epoch: 21 [74240/100000 (74%)]\tLoss: -12491.209375\n",
            "Train Epoch: 21 [76800/100000 (77%)]\tLoss: -12553.788281\n",
            "Train Epoch: 21 [79360/100000 (79%)]\tLoss: -12935.572656\n",
            "Train Epoch: 21 [81920/100000 (82%)]\tLoss: -12594.573437\n",
            "Train Epoch: 21 [84480/100000 (84%)]\tLoss: -11915.830469\n",
            "Train Epoch: 21 [87040/100000 (87%)]\tLoss: -12847.103125\n",
            "Train Epoch: 21 [89600/100000 (90%)]\tLoss: -12597.417187\n",
            "Train Epoch: 21 [92160/100000 (92%)]\tLoss: -11990.191406\n",
            "Train Epoch: 21 [94720/100000 (95%)]\tLoss: -11980.665625\n",
            "Train Epoch: 21 [97280/100000 (97%)]\tLoss: -12606.560156\n",
            "Train Epoch: 21 [62400/100000 (100%)]\tLoss: -8118.162338\n",
            "====> Epoch: 21 Average loss: -125064.5411\n",
            "Train Epoch: 22 [0/100000 (0%)]\tLoss: -117699.898438\n",
            "Train Epoch: 22 [2560/100000 (3%)]\tLoss: -13237.571875\n",
            "Train Epoch: 22 [5120/100000 (5%)]\tLoss: -13084.122656\n",
            "Train Epoch: 22 [7680/100000 (8%)]\tLoss: -12216.450781\n",
            "Train Epoch: 22 [10240/100000 (10%)]\tLoss: -12762.611719\n",
            "Train Epoch: 22 [12800/100000 (13%)]\tLoss: -12695.351562\n",
            "Train Epoch: 22 [15360/100000 (15%)]\tLoss: -12628.296875\n",
            "Train Epoch: 22 [17920/100000 (18%)]\tLoss: -11899.878125\n",
            "Train Epoch: 22 [20480/100000 (20%)]\tLoss: -12875.382031\n",
            "Train Epoch: 22 [23040/100000 (23%)]\tLoss: -11831.661719\n",
            "Train Epoch: 22 [25600/100000 (26%)]\tLoss: -12679.659375\n",
            "Train Epoch: 22 [28160/100000 (28%)]\tLoss: -12247.540625\n",
            "Train Epoch: 22 [30720/100000 (31%)]\tLoss: -12199.178906\n",
            "Train Epoch: 22 [33280/100000 (33%)]\tLoss: -12744.175781\n",
            "Train Epoch: 22 [35840/100000 (36%)]\tLoss: -12796.475000\n",
            "Train Epoch: 22 [38400/100000 (38%)]\tLoss: -13192.101562\n",
            "Train Epoch: 22 [40960/100000 (41%)]\tLoss: -12750.280469\n",
            "Train Epoch: 22 [43520/100000 (43%)]\tLoss: -12241.666406\n",
            "Train Epoch: 22 [46080/100000 (46%)]\tLoss: -11911.321875\n",
            "Train Epoch: 22 [48640/100000 (49%)]\tLoss: -12142.106250\n",
            "Train Epoch: 22 [51200/100000 (51%)]\tLoss: -13074.062500\n",
            "Train Epoch: 22 [53760/100000 (54%)]\tLoss: -12659.645313\n",
            "Train Epoch: 22 [56320/100000 (56%)]\tLoss: -12791.175000\n",
            "Train Epoch: 22 [58880/100000 (59%)]\tLoss: -12175.542187\n",
            "Train Epoch: 22 [61440/100000 (61%)]\tLoss: -11856.200781\n",
            "Train Epoch: 22 [64000/100000 (64%)]\tLoss: -12593.226562\n",
            "Train Epoch: 22 [66560/100000 (66%)]\tLoss: -12377.760937\n",
            "Train Epoch: 22 [69120/100000 (69%)]\tLoss: -12441.498438\n",
            "Train Epoch: 22 [71680/100000 (72%)]\tLoss: -12422.794531\n",
            "Train Epoch: 22 [74240/100000 (74%)]\tLoss: -12599.689844\n",
            "Train Epoch: 22 [76800/100000 (77%)]\tLoss: -12226.128125\n",
            "Train Epoch: 22 [79360/100000 (79%)]\tLoss: -12447.670313\n",
            "Train Epoch: 22 [81920/100000 (82%)]\tLoss: -12396.832031\n",
            "Train Epoch: 22 [84480/100000 (84%)]\tLoss: -12055.978125\n",
            "Train Epoch: 22 [87040/100000 (87%)]\tLoss: -12115.607813\n",
            "Train Epoch: 22 [89600/100000 (90%)]\tLoss: -11980.818750\n",
            "Train Epoch: 22 [92160/100000 (92%)]\tLoss: -12535.639844\n",
            "Train Epoch: 22 [94720/100000 (95%)]\tLoss: -11893.567969\n",
            "Train Epoch: 22 [97280/100000 (97%)]\tLoss: -11607.196875\n",
            "Train Epoch: 22 [62400/100000 (100%)]\tLoss: -8420.771104\n",
            "====> Epoch: 22 Average loss: -124599.3187\n",
            "Train Epoch: 23 [0/100000 (0%)]\tLoss: -121826.640625\n",
            "Train Epoch: 23 [2560/100000 (3%)]\tLoss: -12463.785156\n",
            "Train Epoch: 23 [5120/100000 (5%)]\tLoss: -12530.069531\n",
            "Train Epoch: 23 [7680/100000 (8%)]\tLoss: -12080.022656\n",
            "Train Epoch: 23 [10240/100000 (10%)]\tLoss: -12442.787500\n",
            "Train Epoch: 23 [12800/100000 (13%)]\tLoss: -11914.545313\n",
            "Train Epoch: 23 [15360/100000 (15%)]\tLoss: -13298.542187\n",
            "Train Epoch: 23 [17920/100000 (18%)]\tLoss: -11777.485156\n",
            "Train Epoch: 23 [20480/100000 (20%)]\tLoss: -12488.071094\n",
            "Train Epoch: 23 [23040/100000 (23%)]\tLoss: -12472.889063\n",
            "Train Epoch: 23 [25600/100000 (26%)]\tLoss: -12780.189844\n",
            "Train Epoch: 23 [28160/100000 (28%)]\tLoss: -13137.495312\n",
            "Train Epoch: 23 [30720/100000 (31%)]\tLoss: -12508.037500\n",
            "Train Epoch: 23 [33280/100000 (33%)]\tLoss: -12211.079687\n",
            "Train Epoch: 23 [35840/100000 (36%)]\tLoss: -12660.067188\n",
            "Train Epoch: 23 [38400/100000 (38%)]\tLoss: -12998.247656\n",
            "Train Epoch: 23 [40960/100000 (41%)]\tLoss: -12100.687500\n",
            "Train Epoch: 23 [43520/100000 (43%)]\tLoss: -12353.667187\n",
            "Train Epoch: 23 [46080/100000 (46%)]\tLoss: -12346.492188\n",
            "Train Epoch: 23 [48640/100000 (49%)]\tLoss: -12325.752344\n",
            "Train Epoch: 23 [51200/100000 (51%)]\tLoss: -12279.739063\n",
            "Train Epoch: 23 [53760/100000 (54%)]\tLoss: -12302.247656\n",
            "Train Epoch: 23 [56320/100000 (56%)]\tLoss: -12817.011719\n",
            "Train Epoch: 23 [58880/100000 (59%)]\tLoss: -11808.025781\n",
            "Train Epoch: 23 [61440/100000 (61%)]\tLoss: -12152.339844\n",
            "Train Epoch: 23 [64000/100000 (64%)]\tLoss: -11993.145313\n",
            "Train Epoch: 23 [66560/100000 (66%)]\tLoss: -12514.792969\n",
            "Train Epoch: 23 [69120/100000 (69%)]\tLoss: -12504.151562\n",
            "Train Epoch: 23 [71680/100000 (72%)]\tLoss: -13316.082813\n",
            "Train Epoch: 23 [74240/100000 (74%)]\tLoss: -12185.639844\n",
            "Train Epoch: 23 [76800/100000 (77%)]\tLoss: -12960.241406\n",
            "Train Epoch: 23 [79360/100000 (79%)]\tLoss: -12057.693750\n",
            "Train Epoch: 23 [81920/100000 (82%)]\tLoss: -12357.810938\n",
            "Train Epoch: 23 [84480/100000 (84%)]\tLoss: -11605.527344\n",
            "Train Epoch: 23 [87040/100000 (87%)]\tLoss: -13142.109375\n",
            "Train Epoch: 23 [89600/100000 (90%)]\tLoss: -11987.815625\n",
            "Train Epoch: 23 [92160/100000 (92%)]\tLoss: -12199.310938\n",
            "Train Epoch: 23 [94720/100000 (95%)]\tLoss: -12952.560938\n",
            "Train Epoch: 23 [97280/100000 (97%)]\tLoss: -11599.792969\n",
            "Train Epoch: 23 [62400/100000 (100%)]\tLoss: -7661.347403\n",
            "====> Epoch: 23 Average loss: -124249.8132\n",
            "Train Epoch: 24 [0/100000 (0%)]\tLoss: -122915.914062\n",
            "Train Epoch: 24 [2560/100000 (3%)]\tLoss: -12096.582813\n",
            "Train Epoch: 24 [5120/100000 (5%)]\tLoss: -12729.066406\n",
            "Train Epoch: 24 [7680/100000 (8%)]\tLoss: -12754.242188\n",
            "Train Epoch: 24 [10240/100000 (10%)]\tLoss: -12418.879688\n",
            "Train Epoch: 24 [12800/100000 (13%)]\tLoss: -11512.710156\n",
            "Train Epoch: 24 [15360/100000 (15%)]\tLoss: -13198.464062\n",
            "Train Epoch: 24 [17920/100000 (18%)]\tLoss: -12123.720312\n",
            "Train Epoch: 24 [20480/100000 (20%)]\tLoss: -12766.368750\n",
            "Train Epoch: 24 [23040/100000 (23%)]\tLoss: -11925.489063\n",
            "Train Epoch: 24 [25600/100000 (26%)]\tLoss: -12463.654688\n",
            "Train Epoch: 24 [28160/100000 (28%)]\tLoss: -13372.676563\n",
            "Train Epoch: 24 [30720/100000 (31%)]\tLoss: -12163.864063\n",
            "Train Epoch: 24 [33280/100000 (33%)]\tLoss: -12067.187500\n",
            "Train Epoch: 24 [35840/100000 (36%)]\tLoss: -12285.694531\n",
            "Train Epoch: 24 [38400/100000 (38%)]\tLoss: -12948.813281\n",
            "Train Epoch: 24 [40960/100000 (41%)]\tLoss: -12231.134375\n",
            "Train Epoch: 24 [43520/100000 (43%)]\tLoss: -12267.263281\n",
            "Train Epoch: 24 [46080/100000 (46%)]\tLoss: -12079.927344\n",
            "Train Epoch: 24 [48640/100000 (49%)]\tLoss: -11428.572656\n",
            "Train Epoch: 24 [51200/100000 (51%)]\tLoss: -12293.664062\n",
            "Train Epoch: 24 [53760/100000 (54%)]\tLoss: -13215.553125\n",
            "Train Epoch: 24 [56320/100000 (56%)]\tLoss: -11928.861719\n",
            "Train Epoch: 24 [58880/100000 (59%)]\tLoss: -11946.902344\n",
            "Train Epoch: 24 [61440/100000 (61%)]\tLoss: -11408.196875\n",
            "Train Epoch: 24 [64000/100000 (64%)]\tLoss: -12095.835938\n",
            "Train Epoch: 24 [66560/100000 (66%)]\tLoss: -12712.022656\n",
            "Train Epoch: 24 [69120/100000 (69%)]\tLoss: -12870.364844\n",
            "Train Epoch: 24 [71680/100000 (72%)]\tLoss: -11701.064844\n",
            "Train Epoch: 24 [74240/100000 (74%)]\tLoss: -12165.114063\n",
            "Train Epoch: 24 [76800/100000 (77%)]\tLoss: -12581.298437\n",
            "Train Epoch: 24 [79360/100000 (79%)]\tLoss: -12852.501562\n",
            "Train Epoch: 24 [81920/100000 (82%)]\tLoss: -12139.049219\n",
            "Train Epoch: 24 [84480/100000 (84%)]\tLoss: -12455.992969\n",
            "Train Epoch: 24 [87040/100000 (87%)]\tLoss: -12061.028906\n",
            "Train Epoch: 24 [89600/100000 (90%)]\tLoss: -12258.562500\n",
            "Train Epoch: 24 [92160/100000 (92%)]\tLoss: -12529.689844\n",
            "Train Epoch: 24 [94720/100000 (95%)]\tLoss: -12979.771875\n",
            "Train Epoch: 24 [97280/100000 (97%)]\tLoss: -12783.466406\n",
            "Train Epoch: 24 [62400/100000 (100%)]\tLoss: -8345.960227\n",
            "====> Epoch: 24 Average loss: -123897.9634\n",
            "Train Epoch: 25 [0/100000 (0%)]\tLoss: -119833.554688\n",
            "Train Epoch: 25 [2560/100000 (3%)]\tLoss: -12326.836719\n",
            "Train Epoch: 25 [5120/100000 (5%)]\tLoss: -13154.850000\n",
            "Train Epoch: 25 [7680/100000 (8%)]\tLoss: -11778.823437\n",
            "Train Epoch: 25 [10240/100000 (10%)]\tLoss: -11781.732813\n",
            "Train Epoch: 25 [12800/100000 (13%)]\tLoss: -13074.718750\n",
            "Train Epoch: 25 [15360/100000 (15%)]\tLoss: -12336.273438\n",
            "Train Epoch: 25 [17920/100000 (18%)]\tLoss: -12742.946094\n",
            "Train Epoch: 25 [20480/100000 (20%)]\tLoss: -11747.215625\n",
            "Train Epoch: 25 [23040/100000 (23%)]\tLoss: -12460.195312\n",
            "Train Epoch: 25 [25600/100000 (26%)]\tLoss: -11877.231250\n",
            "Train Epoch: 25 [28160/100000 (28%)]\tLoss: -12151.385156\n",
            "Train Epoch: 25 [30720/100000 (31%)]\tLoss: -12025.866406\n",
            "Train Epoch: 25 [33280/100000 (33%)]\tLoss: -12301.575781\n",
            "Train Epoch: 25 [35840/100000 (36%)]\tLoss: -12245.532031\n",
            "Train Epoch: 25 [38400/100000 (38%)]\tLoss: -11761.905469\n",
            "Train Epoch: 25 [40960/100000 (41%)]\tLoss: -12266.853125\n",
            "Train Epoch: 25 [43520/100000 (43%)]\tLoss: -13004.489063\n",
            "Train Epoch: 25 [46080/100000 (46%)]\tLoss: -12314.835938\n",
            "Train Epoch: 25 [48640/100000 (49%)]\tLoss: -13110.418750\n",
            "Train Epoch: 25 [51200/100000 (51%)]\tLoss: -12215.864063\n",
            "Train Epoch: 25 [53760/100000 (54%)]\tLoss: -12558.842188\n",
            "Train Epoch: 25 [56320/100000 (56%)]\tLoss: -12228.773438\n",
            "Train Epoch: 25 [58880/100000 (59%)]\tLoss: -11756.881250\n",
            "Train Epoch: 25 [61440/100000 (61%)]\tLoss: -12054.637500\n",
            "Train Epoch: 25 [64000/100000 (64%)]\tLoss: -11967.314062\n",
            "Train Epoch: 25 [66560/100000 (66%)]\tLoss: -12324.915625\n",
            "Train Epoch: 25 [69120/100000 (69%)]\tLoss: -11890.866406\n",
            "Train Epoch: 25 [71680/100000 (72%)]\tLoss: -11984.073437\n",
            "Train Epoch: 25 [74240/100000 (74%)]\tLoss: -12073.289062\n",
            "Train Epoch: 25 [76800/100000 (77%)]\tLoss: -12912.502344\n",
            "Train Epoch: 25 [79360/100000 (79%)]\tLoss: -12008.485937\n",
            "Train Epoch: 25 [81920/100000 (82%)]\tLoss: -12589.296094\n",
            "Train Epoch: 25 [84480/100000 (84%)]\tLoss: -11874.996094\n",
            "Train Epoch: 25 [87040/100000 (87%)]\tLoss: -11894.422656\n",
            "Train Epoch: 25 [89600/100000 (90%)]\tLoss: -11887.221094\n",
            "Train Epoch: 25 [92160/100000 (92%)]\tLoss: -12706.925781\n",
            "Train Epoch: 25 [94720/100000 (95%)]\tLoss: -12975.935938\n",
            "Train Epoch: 25 [97280/100000 (97%)]\tLoss: -12194.379688\n",
            "Train Epoch: 25 [62400/100000 (100%)]\tLoss: -7637.275162\n",
            "====> Epoch: 25 Average loss: -123601.7772\n",
            "Train Epoch: 26 [0/100000 (0%)]\tLoss: -128464.210938\n",
            "Train Epoch: 26 [2560/100000 (3%)]\tLoss: -12413.510937\n",
            "Train Epoch: 26 [5120/100000 (5%)]\tLoss: -12082.997656\n",
            "Train Epoch: 26 [7680/100000 (8%)]\tLoss: -11824.321094\n",
            "Train Epoch: 26 [10240/100000 (10%)]\tLoss: -12039.198437\n",
            "Train Epoch: 26 [12800/100000 (13%)]\tLoss: -12210.871094\n",
            "Train Epoch: 26 [15360/100000 (15%)]\tLoss: -11819.735937\n",
            "Train Epoch: 26 [17920/100000 (18%)]\tLoss: -11421.819531\n",
            "Train Epoch: 26 [20480/100000 (20%)]\tLoss: -13035.055469\n",
            "Train Epoch: 26 [23040/100000 (23%)]\tLoss: -12539.325781\n",
            "Train Epoch: 26 [25600/100000 (26%)]\tLoss: -12430.884375\n",
            "Train Epoch: 26 [28160/100000 (28%)]\tLoss: -11851.454687\n",
            "Train Epoch: 26 [30720/100000 (31%)]\tLoss: -13140.328125\n",
            "Train Epoch: 26 [33280/100000 (33%)]\tLoss: -12231.792187\n",
            "Train Epoch: 26 [35840/100000 (36%)]\tLoss: -12558.239844\n",
            "Train Epoch: 26 [38400/100000 (38%)]\tLoss: -12974.553125\n",
            "Train Epoch: 26 [40960/100000 (41%)]\tLoss: -12244.285938\n",
            "Train Epoch: 26 [43520/100000 (43%)]\tLoss: -11672.264844\n",
            "Train Epoch: 26 [46080/100000 (46%)]\tLoss: -11506.625781\n",
            "Train Epoch: 26 [48640/100000 (49%)]\tLoss: -11975.876562\n",
            "Train Epoch: 26 [51200/100000 (51%)]\tLoss: -12502.698437\n",
            "Train Epoch: 26 [53760/100000 (54%)]\tLoss: -12680.970312\n",
            "Train Epoch: 26 [56320/100000 (56%)]\tLoss: -13065.441406\n",
            "Train Epoch: 26 [58880/100000 (59%)]\tLoss: -11867.391406\n",
            "Train Epoch: 26 [61440/100000 (61%)]\tLoss: -12406.453906\n",
            "Train Epoch: 26 [64000/100000 (64%)]\tLoss: -12549.664844\n",
            "Train Epoch: 26 [66560/100000 (66%)]\tLoss: -12213.742188\n",
            "Train Epoch: 26 [69120/100000 (69%)]\tLoss: -12864.671094\n",
            "Train Epoch: 26 [71680/100000 (72%)]\tLoss: -11701.099219\n",
            "Train Epoch: 26 [74240/100000 (74%)]\tLoss: -12085.883594\n",
            "Train Epoch: 26 [76800/100000 (77%)]\tLoss: -11574.055469\n",
            "Train Epoch: 26 [79360/100000 (79%)]\tLoss: -11995.158594\n",
            "Train Epoch: 26 [81920/100000 (82%)]\tLoss: -11996.675000\n",
            "Train Epoch: 26 [84480/100000 (84%)]\tLoss: -11611.387500\n",
            "Train Epoch: 26 [87040/100000 (87%)]\tLoss: -11859.467969\n",
            "Train Epoch: 26 [89600/100000 (90%)]\tLoss: -12040.507031\n",
            "Train Epoch: 26 [92160/100000 (92%)]\tLoss: -12602.378906\n",
            "Train Epoch: 26 [94720/100000 (95%)]\tLoss: -12093.870312\n",
            "Train Epoch: 26 [97280/100000 (97%)]\tLoss: -12299.844531\n",
            "Train Epoch: 26 [62400/100000 (100%)]\tLoss: -7662.881494\n",
            "====> Epoch: 26 Average loss: -123332.3073\n",
            "Train Epoch: 27 [0/100000 (0%)]\tLoss: -127239.203125\n",
            "Train Epoch: 27 [2560/100000 (3%)]\tLoss: -12448.417187\n",
            "Train Epoch: 27 [5120/100000 (5%)]\tLoss: -13118.493750\n",
            "Train Epoch: 27 [7680/100000 (8%)]\tLoss: -12395.289062\n",
            "Train Epoch: 27 [10240/100000 (10%)]\tLoss: -11933.149219\n",
            "Train Epoch: 27 [12800/100000 (13%)]\tLoss: -11855.796094\n",
            "Train Epoch: 27 [15360/100000 (15%)]\tLoss: -12720.002344\n",
            "Train Epoch: 27 [17920/100000 (18%)]\tLoss: -12308.700781\n",
            "Train Epoch: 27 [20480/100000 (20%)]\tLoss: -13353.329687\n",
            "Train Epoch: 27 [23040/100000 (23%)]\tLoss: -12812.281250\n",
            "Train Epoch: 27 [25600/100000 (26%)]\tLoss: -12637.185156\n",
            "Train Epoch: 27 [28160/100000 (28%)]\tLoss: -12002.792969\n",
            "Train Epoch: 27 [30720/100000 (31%)]\tLoss: -12664.129688\n",
            "Train Epoch: 27 [33280/100000 (33%)]\tLoss: -12248.560156\n",
            "Train Epoch: 27 [35840/100000 (36%)]\tLoss: -12219.010937\n",
            "Train Epoch: 27 [38400/100000 (38%)]\tLoss: -12112.233594\n",
            "Train Epoch: 27 [40960/100000 (41%)]\tLoss: -12077.137500\n",
            "Train Epoch: 27 [43520/100000 (43%)]\tLoss: -13091.518750\n",
            "Train Epoch: 27 [46080/100000 (46%)]\tLoss: -12679.785156\n",
            "Train Epoch: 27 [48640/100000 (49%)]\tLoss: -11897.939844\n",
            "Train Epoch: 27 [51200/100000 (51%)]\tLoss: -12097.264063\n",
            "Train Epoch: 27 [53760/100000 (54%)]\tLoss: -12600.398438\n",
            "Train Epoch: 27 [56320/100000 (56%)]\tLoss: -12398.037500\n",
            "Train Epoch: 27 [58880/100000 (59%)]\tLoss: -12617.699219\n",
            "Train Epoch: 27 [61440/100000 (61%)]\tLoss: -12410.631250\n",
            "Train Epoch: 27 [64000/100000 (64%)]\tLoss: -11992.589844\n",
            "Train Epoch: 27 [66560/100000 (66%)]\tLoss: -12262.821094\n",
            "Train Epoch: 27 [69120/100000 (69%)]\tLoss: -12281.730469\n",
            "Train Epoch: 27 [71680/100000 (72%)]\tLoss: -12092.238281\n",
            "Train Epoch: 27 [74240/100000 (74%)]\tLoss: -11941.079687\n",
            "Train Epoch: 27 [76800/100000 (77%)]\tLoss: -12192.317969\n",
            "Train Epoch: 27 [79360/100000 (79%)]\tLoss: -11703.356250\n",
            "Train Epoch: 27 [81920/100000 (82%)]\tLoss: -12758.165625\n",
            "Train Epoch: 27 [84480/100000 (84%)]\tLoss: -12277.378125\n",
            "Train Epoch: 27 [87040/100000 (87%)]\tLoss: -12595.019531\n",
            "Train Epoch: 27 [89600/100000 (90%)]\tLoss: -12759.628906\n",
            "Train Epoch: 27 [92160/100000 (92%)]\tLoss: -12345.875781\n",
            "Train Epoch: 27 [94720/100000 (95%)]\tLoss: -12423.660156\n",
            "Train Epoch: 27 [97280/100000 (97%)]\tLoss: -11293.400000\n",
            "Train Epoch: 27 [62400/100000 (100%)]\tLoss: -8129.574675\n",
            "====> Epoch: 27 Average loss: -123143.8876\n",
            "Train Epoch: 28 [0/100000 (0%)]\tLoss: -125614.390625\n",
            "Train Epoch: 28 [2560/100000 (3%)]\tLoss: -12069.822656\n",
            "Train Epoch: 28 [5120/100000 (5%)]\tLoss: -12373.996875\n",
            "Train Epoch: 28 [7680/100000 (8%)]\tLoss: -12973.732031\n",
            "Train Epoch: 28 [10240/100000 (10%)]\tLoss: -12910.904688\n",
            "Train Epoch: 28 [12800/100000 (13%)]\tLoss: -12550.704687\n",
            "Train Epoch: 28 [15360/100000 (15%)]\tLoss: -12418.110156\n",
            "Train Epoch: 28 [17920/100000 (18%)]\tLoss: -12550.573437\n",
            "Train Epoch: 28 [20480/100000 (20%)]\tLoss: -12177.034375\n",
            "Train Epoch: 28 [23040/100000 (23%)]\tLoss: -12393.926563\n",
            "Train Epoch: 28 [25600/100000 (26%)]\tLoss: -12439.632031\n",
            "Train Epoch: 28 [28160/100000 (28%)]\tLoss: -13057.392187\n",
            "Train Epoch: 28 [30720/100000 (31%)]\tLoss: -12541.087500\n",
            "Train Epoch: 28 [33280/100000 (33%)]\tLoss: -12607.389063\n",
            "Train Epoch: 28 [35840/100000 (36%)]\tLoss: -12005.450781\n",
            "Train Epoch: 28 [38400/100000 (38%)]\tLoss: -12666.790625\n",
            "Train Epoch: 28 [40960/100000 (41%)]\tLoss: -12425.631250\n",
            "Train Epoch: 28 [43520/100000 (43%)]\tLoss: -13039.132812\n",
            "Train Epoch: 28 [46080/100000 (46%)]\tLoss: -12525.043750\n",
            "Train Epoch: 28 [48640/100000 (49%)]\tLoss: -12659.806250\n",
            "Train Epoch: 28 [51200/100000 (51%)]\tLoss: -12475.292187\n",
            "Train Epoch: 28 [53760/100000 (54%)]\tLoss: -12302.829687\n",
            "Train Epoch: 28 [56320/100000 (56%)]\tLoss: -12475.813281\n",
            "Train Epoch: 28 [58880/100000 (59%)]\tLoss: -11828.278125\n",
            "Train Epoch: 28 [61440/100000 (61%)]\tLoss: -12614.787500\n",
            "Train Epoch: 28 [64000/100000 (64%)]\tLoss: -13369.137500\n",
            "Train Epoch: 28 [66560/100000 (66%)]\tLoss: -12869.742188\n",
            "Train Epoch: 28 [69120/100000 (69%)]\tLoss: -11721.380469\n",
            "Train Epoch: 28 [71680/100000 (72%)]\tLoss: -12506.124219\n",
            "Train Epoch: 28 [74240/100000 (74%)]\tLoss: -12322.851562\n",
            "Train Epoch: 28 [76800/100000 (77%)]\tLoss: -12287.302344\n",
            "Train Epoch: 28 [79360/100000 (79%)]\tLoss: -11667.850781\n",
            "Train Epoch: 28 [81920/100000 (82%)]\tLoss: -12453.754688\n",
            "Train Epoch: 28 [84480/100000 (84%)]\tLoss: -12102.201563\n",
            "Train Epoch: 28 [87040/100000 (87%)]\tLoss: -12130.786719\n",
            "Train Epoch: 28 [89600/100000 (90%)]\tLoss: -11854.893750\n",
            "Train Epoch: 28 [92160/100000 (92%)]\tLoss: -12795.245312\n",
            "Train Epoch: 28 [94720/100000 (95%)]\tLoss: -12493.898438\n",
            "Train Epoch: 28 [97280/100000 (97%)]\tLoss: -12435.450781\n",
            "Train Epoch: 28 [62400/100000 (100%)]\tLoss: -7611.420455\n",
            "====> Epoch: 28 Average loss: -122906.4922\n",
            "Train Epoch: 29 [0/100000 (0%)]\tLoss: -124039.234375\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-8bfa765c75d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-9-d70b54ed30d5>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mcount_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbatch_count_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0;31m# print(batch_idx)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mcount_data\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m             \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_transform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(img, size, interpolation)\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(self, size, resample, box)\u001b[0m\n\u001b[1;32m   1743\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1744\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1745\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m     def rotate(self, angle, resample=NEAREST, expand=0, center=None,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0d3cRvRq1my",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filename = \"vae_celeba_data_50_epoches_150dim_latent_space.h5\"\n",
        "import os\n",
        "if os.path.isfile(filename):\n",
        "  model.load_state_dict(torch.load(filename))\n",
        "else:\n",
        "  torch.save(model.state_dict(), filename)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTIIsTNHYn38",
        "colab_type": "text"
      },
      "source": [
        "See the model output that compare the original image with generated image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODhnG1JvBBVQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "dd3ef1ad-157d-411f-aa7d-f57d0cdb75d7"
      },
      "source": [
        "from torch.utils.data import RandomSampler\n",
        "from torchvision.utils import save_image\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "with torch.no_grad():\n",
        "  test_loader = DataLoader(celeba_data,batch_size=16, shuffle=True)\n",
        "  itr = iter(test_loader)\n",
        "  # next(itr)\n",
        "  batch, _ = next(itr)\n",
        "  batch = batch.to(device)\n",
        "  model.eval()\n",
        "  recon_batch, _, _  = model(batch)\n",
        "\n",
        "  show(batch.cpu(), renorm = True, nrow=4)\n",
        "  show(recon_batch.cpu(), renorm = True, nrow=4)\n",
        "\n",
        "\n",
        "# epoch = 1\n",
        "# n = 5\n",
        "\n",
        "# comparison = torch.cat([batch[:n],recon_batch[:n]])\n",
        "\n",
        "# save_image(comparison.cpu(),'reconstruction_ecommerce' + str(epoch) + '.png', nrow=n)\n",
        "                \n",
        "# plt.imshow(mpimg.imread('reconstruction_ecommerce' + str(epoch) + '.png'), aspect=True)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8PdHlNWO6IKZTYrFwEFFwlBQMpKQVMSUQWTEqBu29dgsxRxo6BtBsesZ3//o7\nvPOTd1iUJY1p8V16xm95X8KDFIEsERwOMm5dOeDNl495/cZVbuztsDfIyROJJiA9GOfQWrBoSuZN\ngz1dsvTd/MqkA7YMznlAIKXGtpayWlE2LVXTgIDTs3MaYy9ZFtc2lNMzslQSfA3KYYTAW1B4vIfW\n+VgEjkQnCcPBiCIfUqQ5iUrp01w9MT74QLBdDa4LkQEkA7ZVWB0BveA90juUt+TecXVccLQ74mBv\nQlGkzNgg1lEmn3Bfu7EF1L9QumH7+5v12qtlh7MtbdtQFBk3X7rG9ZdeQquUZjEjtA1KCHSWMZyM\nEaEFWxFMi5MKKxOUsigVSESgkCJSHqWMnGQtkct4L1wwOOMAgQ1+3dIHsXFq/5MIZx/I9mjThvwc\n1v1lnHU466jLiuBcN3OBXCuuH+7w2q2X2NvbIUnTGOPRl0L5dWwqVQYuIGUkbSulSdOc4chxYOHa\n4ZL90SMeLWraZtPGgv4GBXA+cneV6Kyh6BkngmA9VRMXfY/md4D95iZ3ie22bfjJj9/hO9/5C05O\nTmjadu2ShRDrT7WSFHnG/rjgyqTg5uGE144Pee3aIdcOdtkZDcgS1ZHUHd5YhBcIpRkWGcd7E14+\n2mNaO1ztcDLOikKvBdOHgOyUXNNaWmMxrWEy3uH6tRsMBiPYaqPinKWpShQJQrh1cUHf7oPQ5WK1\nQqiA0pIkEeSpokhyMpWSSIl0DiE0skOfhVIgE5CxmkVrHRH2jtUDMa4WLnoOKYGhlgjbRsZQ6MIP\nNjm+tWV5Kk3X3ZwXHU9L5/pNby14x6AYcOXqMaNiiG0bbFPi2wrhHAKPThRZltGmCS7VEZ11Cc5Z\nMu3JdKC1YIKPIYjWBBFompZaQ60lLiiCi+w363pGeLiUFtRaf8FuLaypYr3P7L3HtLHMx7QG19Gv\nXGtiMj9Et6JQgRtHOxztj9H6cvoFWMcUAoUSKUIF0BHp1drhTFwkWisOxgXX9kfcm1aszOUyIiEE\nIgicD9gQOaJRdwYcUTid3wIl+gV7yaWi8+gcjx494s///D/w3nvvUVVV9AZE7H+UKcneMOflq7u8\n9tIVXjqacDjKORjkHI0LJsOcPElRGugKTaRQEDzeRPpilij2hwNu7k14cL7kol5gOqK4kuryAhYi\n9htKEuqmxVjL3t4BL7/8CkUxjIF9EOs8sXUN1oOWgtgRQiJlQIlAkJIEhU41WVAUxZAiS8m0JktS\ndJrFCgwRq4dqAQ4w1lG7miGO3UyihY5ofE8QIaa06NhdIjiUtSxOTqhXS8KlMrRtqxheWA6fSWh/\n6vkmpRd8oK1rXNMwGow4OqkKb3gAACAASURBVLpGojVtXSGCj2CiMR1DLKBUTIn5PEUlOiqvXOJT\nhREtVljausEaR46GXGNHEYisjcFYjyGwWq2Yz+eRNK/1OqxOkoQ8z1/Yer5QsbXsFqXWal2g2zNg\nTNPStm2subOxNEhISfCSREiu7hR85fohKktY1oaEhpyEQhUkWkWIWdI14VJ4EdG/1sG8tCwXFcvF\nimpV4oJnd5CxmylOgouxZOgzfNHqmBAwBBLRQek+LjKpZZx4HLKKbnfogKDNiOSApm555+13+Jvv\nf4/ZdBo5n0GAkKRKcPNgwjdvXefNlw74ytEOw0KhJRRKkycapSUikaC6xmIy8leFBhsE3kZNPRpk\nXNkdc7w75s7FknnlcEEQtI2dBzvLEtdxpxwRaKXZ3dtnd2evy3n2zVc6dN1avEs65pPqLG8kCkgh\naFEE4SkGQwZZQSoFWVKg8wJ0SlCa1htmiymL5RzhLJpAJjSH4yE27HEoJmglaCsBMiYM2tbStC3G\ntLHeE0G1KrGt2VpNL+aufuZ6fFaeM/S6vreYYY0hVHWFd4ZbN29yeHBEXVbMzk4olzNW0zmurhgW\nGcNBRnAxHytkIElSJIGm9Jw3Kx6vKqoqlrOFEBF5laSIscaHQNm01K3Di4C1BtM2bBlNALTWFEXx\nxQlnby3SLI1d6vIs0vFsdK+qssK0kWOpQyCRCUprGusotObWtT2KQcbbH9xj1t4nqJzhoODKwQE3\nrx1zfLjLIE8QUmCdY7Zc8OGde7z/0cd8+viEcrHA1C0hWFJhacuWkZYkIhZDC3SXxwukKvr0NsQs\nleuYMlHiJR6wftNBTQRi8EhvND3eBT59+Cl//Vd/yb1P7kLwDIuMZeuw3jMpEl67ccCr1/bZLTKq\nxjAtl/jgGWQDdoaSPRJ2lGCUahIR4zYPlK2nbaFxgWVdsVotaZ1jMhyQZwl22cZYFt+xqTxK9oyf\n6D4qGfPEw+GYNEkjyLKm83WleW7TJUHqaCWTRJHIgFCBNMRYKhY1W0SSUuRD8nyAUAmt88xnM+az\nM6pyRlXOcKYl0xnlcoK3C7zZZZRlOGNofUPVWubLhsZZsixBJpoWAToFlbDViOYLH31Ic9m9jdTA\n1lrQmuNr18gTzfnJKWenDzg5PeX05BxTVRzujLh+dZ9CJTjrmS8XGKE5X1R8ej7j4cWcZd0gCWRK\nkOg0Ng0QHpFp9scjpsuSi1VNJT1IiVJ9v6DNSX3hwikE6CQhLwrywYA0z1BS0tbRhXJd+4m+peLx\nKCNVknttzWSQcO1oj6aquJhVGDTWO+5XDe85z1euHvLr3/wqL9+6gU4TytWKDz+4w/d++BNuf/wQ\n7wWjLCPXsXWmMxasIUsUqVKAAxnIguegkByNChof8LaKAFEH3Ai6LnsejLFr2t8lYn4I+CCoq4of\n/+QtfvjDH7BaLskTyf5kgJwuqYzjcHfI0d4YIT2nyzlByphzDQG7MIiTBaMs4cbBmFvXDjnaGZMq\nzbysuf3gMSezJcvG0PgWITypDzTOobTsev+IdQreuYjWJjqmVvpKmixLKQYFSusnXMKO+NChVX0x\nttISncoonDKQOIWSlrKuSUMgyTMGRUGW5AgCq9WMajUnV4piMKRVjqYpaZuW+eyEh26JraYc7ozx\ntuX04pyTxZKzZYWXkv3dXfJixMWiwgsFUvcZzBcmFHx26dXnjD5iEZvURZrlpMUAnWjaeoGt5kjb\nINsa39aYtmK1csxnimS8h3OB8/mMk3nJamVwXrCX5YzTlNZGQrz1BhcCTniCTEmViowyDTrEcMK7\nmPfsyR8QunuXfXExp5ASnSZkRU4xHJLmWURBnV+3kuyPpZXmjeN9rk4GfDdANky5cWWP67sDjJFU\nraU2NWe1o6qX2HPD7H7CaqLIx0MuHj5i+vEHDJbnXM8lTud4BGlwDDTkgwyXS1rXMkngQgQ8jiuj\nhF+5PuHaeMDjyvHxicXY6BoGHzskSKlItIpoa9X1LwiXH97D6dkJP/jB97j/4GOcNeyORtw6mLCb\nSqZVw7XdMeNBjtSRQhccGONZtC21DQyznKQxPHx8gXeRAbU33mFVNSzKmvmq4XxVUzpDPsjZzwqK\nImFvOCRTc5bGEdhwlGOvow0Yp5Rib2+Po6MrJEmyob11dMfu1bpcC2IhwqZWJ1rWpq5xxuOEIE0U\nRZaRyATnaky9xJqKprXUqxX4kkJDRuwM4UKCTMZM9q5RrS44vX2bu49PaWXC7u4+SZLFNEbb4EyL\nNS29adtOx7/IIhXiacDo0udsCzJrfEl0a/dgf58bV6+QYFnOzzg5OeH00RkPPz3n4eMTbNPQTgaM\n8yHXD3Iaa1jVLZ+enWOdYm/3iPFwl3nTcP/Te8yXNd6D0ilKebJUIHXKpCjYzTK0aBnbhjtv/UdO\nv/1trr708qVr+ULznFJI0jSlGA4Zjkdkadp1cbNr5DaWI3myRHPrpav8yq3r1DJl5QzXD3fYzRT3\n759zsmiYO8Fi1rCbZuyMx6SZRqcpeTakLRvMqmKUDxnsTlg4xXt37kZrieVgpLi2O+KVowFVs0Nr\npzyel1wZZbx+ZYfDUYE4b3h0rjHWrXv8+BCBCiE3VikCP5frVNu25aMP3+cn777DarUAAnuDgq9e\nO6I9mPBwumQ8KhhlKbujIQo4P5tTLhsuqpLGefZe2uFgPATbUJYly9WKyXBEIDDIM4a5YbYqmU9n\nrJYlvigoUsHBIOdgMGBZLrDBdhzdaE0hkGiBSxRJmvHqa6/z8q1XYmOyzqXdtjBR0WxZHB+v1RBi\n5wOvyZTGKYdSCVmSk6uEFIlDUSQZVmfMy5b5YonwK/KdIaPBiOFkQl6MGA52GIwPGe2MOV7OObPw\n+GLJxXSBsIYiUwgvSGSsUFnH9i9gCC8BOz8Du2YDNm72NBgWXL+yR64cs7NzHjw84WxecXte89N7\njwlNxUv7E64eHZAlkjZYvIfGWBatIQ+Cw8OrrBYljz/8mKqOGkBbxzDzpNqRKDja3SF4R9s07BUJ\nn777Q07ufcSVGzcRKH6ebsTPF04pSbOMwXDAYDQk0wnWWpqmiZSurhIkCInAI9OE0WTI8f4QpxRH\nOyPGeUZjFflOiy5GBCcYFTn7e0PG+wWjw2MSPSDNxox39ti7kpNPDmg9HE0SmmpFOZ+TSM94VJCl\nCu8kZRtrQjOt8NaRao2SntaF2Nqkc2s9XXmasZTGXqKRbaymZza94MfvvMX9+/cw1iKAJFHsDXKy\nnSFpluFw5IlkUmQM0pRBkrJbNxwbgxOSw8kuk0ThjKSpDcpZpLfkWrA3HhAI6ERxsD9BqoRUCQQt\nk0XN42nJg4slq9YhRF8Y7fDBkkhJnikOrlzlG9/8NY6Ojrsc2qbS4/Ltj69i9zuHlQ6pQIeMQUh5\n4/pXmLdg0eztXyXRBdIFNJrd4T47gzFmr+Xm0QFCVAyLhDwdoFSKaQMiJNSVY3en4NWbv4Qi4+MH\nj5gt5yjpyZKUYjLkyv4eWZpQVc0mL/w5lmMdP26oQp8p0M/ci/CXvpDlGTduXIP5OYvlkr3JBCE1\ny8ZyOh5Bobm6N2ZvXJAqh/ANuZAMkoTaVHizRLRzBlhemqRUqcV1BRapFGQqkMjAMM8ZZFepVwuy\nYJkkAhU6L6gLN/pr/+IsZyec+XBAPijIdIq1hrqp0R0PNDbtCrRBUJroHu4McpJRwWRUsLe7y+6V\nK/jgSdICnRTIJEWnEqEhK3YITpMNRkz2xox3xhTjHQKCawcZbbmirqoOHTbMp1OapuXG/pCH05JE\nK4xxtMZz0RqWpnPfJIQeAPLxHL3r+8KGp6zmvfv3+OCDD1gul0CsyFk1LatVyWgyYn8wpHI1iYrx\n2yCVjItJjD8QCJkRbMA3NStjyVxA1wYWFdoLCgLXdidc3d/FCxl/WEh62rrk7HTG/bMZw0yxbGIr\nyb7CxTpDqlNGwzFvfPXrvP7a18iy/LK1XGPWWyPEkjRjHUjQLpCGlMxIDg72sdkIkw7IB2MiI8rF\nZLtKyNKcZCwIexMELYkOaKkpK8vKOLyXVKVjnCWM0zGvvvQVDvf2mJdLPFDkOa1znJ7PeW/nNot5\niX3GD408FVuuZXKDBTzTdl6+0GfsN/6bJCm7O7tU1QI3yDne32UgPGmTceWNr6Bl4Hh/ws3rx+R5\njpaKVEnGqUaKjIFosad30c5xa+ixeUYQGuOhdQGLJEhIlCRLEsxyxmo+Z2d/l22r37vfX2hVilSS\nNM/Iivg7G3ma4n1G07akRY5KkkjvQzAYjlHZEJnmjHfGWAVBJehiQDocotMUpQukyhBKYF1DcBYt\nMjyKVCVoGaGDLM9QaU7wQ/zODtbGLm+Liyl1VZJoGRPnqWSSpxzsjDFCcG+5ZGk9I9nTwBQg8V0q\nRKuuLUennfu4bjGf89GHH/Hw04frpk7WBaarhtOLFUfpkCzReKHBC4IJKBcYpLFjXWQaKRrRMKtq\nnDEop7C1p5nVSJWik9iSP81zfPAICQGHCQVy4Lk6HjEpMh7NmggACVBKdq0qA/v7h3z9zW9yeHi8\nbty1zUFdx5xdGiZ078e60bhUBlozHk9Q3uFszWiyG0khLqaVfIiV/4JYn6lEgpKKVEeearNYYqxC\nqCzG861FJ448k4x2jrjir2JcZGfNpmeMpWScJCgEz2xwuXWeEBVijxy9KHj0ucM7qGsyJSj292mU\nJicw1HB9b4KSgp3dEQdXrkaAVzyKDDOhSFSKDAEdHLuDlONJAUFhrGPRGOZGUDlBG2KlFkKQSsm8\nrriYTtcdRLZJCJvOFc8vuH4B4VQkaUqWZhR5QZHnnaUxDIYj8sGAuqzIBglfe+N1hFJUzjLZG3O2\nWLBqDM57lFAkKlpNpXT8rRDjwIPIFFJqEqUR1lMvS0Z7jmyYxqoWZ9GmhtahA8juF89a49hJEl6/\nusfR/ph3Hs34+HxOZQOFlgjR9U9do7IBt+Ul9cwZYwyfPnrERx99wGI+iywdqfASZnXLp4uab97M\nGaQJtom/cWJNwLegtCATEhkEeEcwFtqWpmpRgM0FNiikEwQpUEGSCdV1So+gjZIpg9SyNxozyAoE\nMwKRy9rxCQkhkKQJ48l4XXS9jTb7cElHE9lF/bWDD12NbapIBzmLiyk+Kcj3j8H5WLlDrD01tqU0\nAeESBmmKTjUYy9nJI87OZySjfYaHIwbDDFPPmZ48QmnYO7zKaLKPSxR1WRLqFhXAerfuIh/nfVMG\nFrbe69+J7Lsu8hSw7nl6aTwvhtuQXGQIZFkWOwt6EdFa3zIzF+hUo4oCBiOkjf0fXQClcxIcZVOh\nlWd/d8gkTxAB6qaN61JJhCV2G+zy9XmWMBjkFDs7pMVwXY/cn+0XCggprcnynDTNyLOMrCiAwKBp\nKAYDBoMBdmL42utf5RtvfJVP3vkB7yWGr7/+CuNBjPOcqcE0OBXb9guvccYQ6pogFN630CGveLCr\nFfX0nCxP4s/VGYut61gIXVe0po0817bh+m7BwaTgwaLhbz855fGqQRO77Gm2rEjPGLJ+/RMDvdVc\nrVY8fPiQx48/pW1qJN3PGAhPYx13zi74+HzKzf09lm1EYCd5dKMjzc5HQVt3mRO0baRvNV7idIqx\nHm8MhTHgElQaa2F9iMwakXRJbaHo23ZIGT0S72Kn/LJcMZ9PcbYlhOKptRqZKCJawu43PmNKyYPr\nWnhqi+1ak6ZZhgiRIujxtM4gnUOHSGmzJuCEwwVJ2ay4uDjj5GxKO11yrAXK56TlBfc+eo8iy9Ei\nRasMlee4pqSpKoJUlKaNVjPyAy4Jj3jyIsSmzWffHtW6nz8/KpRGpCluVWOMpVouWU7P+fTeQ+az\nC3YP95lVDeeLip1Ec3F2zsOzBY/nFVrHfkuNtRztjSkU0DXJJgSEc3jb0UiFRoqAUpLJ3i5v/vqv\nc3zzK2gRfxyqR+D7NiUvMp4rnEmaUgwHpFmKTlOSNIFA10lMkSQJr736Gv/tP/vn7A1zbv/kbf7q\nrfe5fzLnYDygePMmx0eGpq0ichcSgtAIL3BNw2w6JZ3OUTrn9JN7LC5mKBlYTs9Jh/F4tjWYuqFa\nrqirkqpqWFSOxyvDvHXcrSz3Lio+Ol9QhsCws5CCDUE9dC0zRdfFIQqmxZiW5XLJycljLi4uYpeG\n+JUY74XAx7MF//btn3IwGqEyyas3DxkPh0zwVFiED6RrCD+QyJiHXS5Lzv0c6eMPE+a5hlFOsAZU\nQGgNUkMScEqx8pbG2VjPKnrjIREyLs6yLDk7O6VuagZ+fOkmr+2mAK0lSnXlAMHhAzgTaI1htlyw\nHNXs7x6gkgHetgQVXc5gDJiGBA9K0gqLdIY6GBpjkEmBEStOpzOS83O0TRn5Ep1oitGQgKOaXaCr\nlHIVf3N12bSsaktYkxCeFR/2rWgiVaGf+3UzN9znL9LPS7VojdeK6aNPsBdnLMuG09Nzzs9mDPIU\n0CzmJavFkhWBT0/OeHgx57xsGWSKIos1zNPZHFvJWKgRApXxXFSOeWtphSIdKhIhUVozGR7yS29+\ng93DI5Ab97XPc34xJIQtAoJOU4QSa54ggHee3d09/uAf/gG/9a1v06wW3HjldT64/TF3HvyUa/tj\njq7scuPGEWnWxsXrPF6DTHJcktE4KM8vsK3j7PyCRdmgM0kwlmSxItUSrKGtK6pVRVkuKauKeWm5\nO2v56fkS4x3WeWwgli8RcEHFDoBdvUr0/Tc/HRhpbo6maTk/P+fx4xOqKv640KYnTwAXqHzgw/MF\nd87mDIcpw0nB8YFhYVpSp/DGUjiJcpF4niVwuDtinOVkScF4NEBryHNFmmoIsU5SeElIEoKEhsDp\nasmirkmSdN15QsrOhAJNU3N+fk5d13gfOgvTKR+/idWkjAIqZMAHF/OZ3tMYQ+s8jy/O2bm2R6ZT\njDMEoUBpgiS28HCxG6KVnoqGEAxBp9h0QDLZI0dijcUYUIXm8MZ1iuGQoBTL1QyxgHndsihLZlVD\n6yWIBISJbKYtn7YXRCkjqinF5qct1j3sLlmacOnZJWPMZTkNAErjsoLpfMbi7oesWk8TFEmRcnB0\nyLXjY24kKU1VU56ecXToeM0aTi7OCN6ghUILRVm2lKUHFM57autYmkDjA1InaOdw0tEax2B/zO6V\na8g067F06CznYDBY57CfNz5XOCMjJVYxeOcwTezfqZDY1pKnBd/8ld/g937n95ns7tEkmq9+9eu8\n9cO3uZgtMATa1rEoHVnmSGTfaEri2hh3pftHuLpBrFbsHB/h0hAbUwXF6awkxZNg8aZmVZWs6oqy\ntcwqx1nVsOx+VVmGWMoFXekYAhW6H6MTrAVzc5/jwl2tSj59+JizszNa014mnIfIghLeE6TCBqjr\nlum8om4tZduQ1gLrNa0QJMSmV0JKkjRDkZCpFBUg1SlCy/+PvPf4sSxNz/x+nznumrg3TEbayixv\nurvYhhRtkyCnh4IEzYAzI+gP0E5LbQRoJa210WKA0V4QIAGCxogCOMKAGLohm23YVd3V3eWyuqrS\nh7/2uM9p8Z1zIzI7qyqbpAABOomsjIi6ce8x3/u97nmfBytAqMgQGKdjYmV2sS45mM1Z1WWkUNE6\nIhxCJN0OxNnEslzSNHUHrpDd6ozcNjGt7jYhKTrh29Dx0Rqa1hC859HJKUnIuX75BiodoqRDqyif\nKETAeheZHUKLEwErFRZB4xtK31CZCt1IdHqJ0fYQLQSrtsG2BtnWhHLNvGpYNp55afBqiMoE3jXg\nO4aMzZBfuCByzCacjTrfHfxN2M0ze3qu+dmuMyAhGyGzCUZIggoEE2iNpRjmbE3HpPmAcl0jWkua\npngV0MLQNCXOQdsKGhPoaWga52mChESTdgUfSRwCSMYTsukuaTGKsEvBZo5VKfX3B98LxAdbNQ16\ntcIbR6Z1fODW89Uvf5V/8Du/x5XL1/AEdJpy4/oNnr91i3t3P6ZIFTI4qrqlMgW5MSjnKJuWplkg\nvCSRmgSJ8pB6TUZB08SwwaWGylZo3xJ8S1U3LKuWZRs4bS1LZ/B4+gG0vpngIc51dmqcgYijjTw+\noTNMQ9UsWC0qHh7c5/j4kLqqNtSNUdSVjV6mILYjjAvMVyWNiQPfVdvGyRwpSIQktIFq0bI8W9Eu\nSpQNaCXJBgX5Vs700oSd/W2KURYLVqamNi1H8yVHZ0vqpsUHRaKyODnRVzNFhPNVVRlZ83ynk8Km\nUBuvfhMiCgQdRaextK3thuAd3i84XGQMxtuMk4JcxKqxlKDTJHrTLkQKQmKFZlm3VE3F7PSYBw8O\n2Nra5vkbV0iKMZrA7PiMsl0zUp5gG86qFXeP13x8/xGN8QiVoWVClMPovX7Evwpnkc4CPsKgVawG\nC52hpEbagwvG+eQa/WzTFN0allKTjbcZjndI64rFcsm94xnjyYwiGzEeWowzeBWwMjJbJEmC9Rkm\nBNbWcrqMbBKTUYrQsSor6djz8YQA2WjMjdfeZF2tOTs+xLU1Mtd9MeDvOawNcTZtvSihDRhVYVtD\n2xi+/JWv8M3f/CbXr9+MGhze4bVma2eb52/d4p23R6QqVjDXZcWgLsiTjER5mqalmteYhSFUnlSn\nSKCpK85OZhwvzkiGiv1rEySum3xpWNQ1ZyvDog6k4ylb2xVnzXEcAO6fRnfi573MzguKHhEUX2Gt\nZTY75eGDQ+7c+RmnZwfdTum6NoZ47P0iDC4a/umq5Hix4truCB1i6EtQGO8o5xXHD+ecHcygsuRC\nxYHedMVgUuAQyDzDpZJURbTVWV1z7+SMg9MlbRuJpZQUaCU2o2/BR/b6ppMIOIftnRe9+sUbgohE\n6R31Zts62qaDNEb4PzKJ0yeISIgWRy0lKskI1oEKJCpFK0nbOprVinKxwK5rMCGCCrxAeEGWZx2I\nY47ayiB4zuqGt9//kPd/dp+1CSBUnA9VKtKD6gSZJHFul4AMnZ6KAJGkqLRApjkiCNrV4pkW81OX\nsHcsV3MWtkZlKYlryXOFR/Kzuw9ZNYYXX3+d8WQbMZwQ1ivGyYAw2maxXHBwOuf09AEPVhXT8YBR\nkEjReXrvQQoaFF4nTK/e4OWvfoP79++QpPkm8jmvoP9ieilfjK01nrCoaEqDlYqmbnj+hRf53W/+\nLi/efLGjrIxlf6kUWZFz+cplLu3ukro5SkJdVSyWa/JEMsgTdKrY3ZvCSGLXDi1TcB6zLBG1w7uK\nZCwZ5QrTStYuUDWWdWVYlBaRjXn11k2YXqJt3ubo6BRDPxfKptd3cUv1IURhIxFnWZrG8vDBCbdv\nf8L9+/cpqxWtqbHWnVdLN5PrcWxLdgyCy7rm04eH3NgZsr8ThXGRcXJEZorJpRFKQLuoSFzXAx4X\nXLq+x/61PZJBSs8XVBnDg9MZ7999wNFsGe3c+0i5iYyE0lIQQtfmaNtN+H2RZ6j3sCHEirQxtsu5\niUAE47BOE0QKaoBWgxg2OgfWRMZErZFCo9IUGUDrbhhbOsYjQ4InFYI2SILQeBuQFrZGI55//hat\nb4CG2gaOl2vuHZ1Q9r2+EOlLLRKnNNamJC5H5AN0PkSnBVKl0IkpCZUgZQLOspLJL9z07D2qQJIP\nttm6/jKuqSnvfMh4lPLq89dI0pxivEWaDslG26g8Q01qBk2DLrZo795FnTVkSrO3NWQ8jMRmznow\nkb/WIwkqIS3GPP+lN3n1G7/Ki7/0y2RZgkryro97oXD3CwD5v9g4vUfYTolYal565RX+yR/8U772\nS18jTdOYl4lIdiWkINWavd1dru7vsz5ckwnQ3lPOlyy0ADmiSDXZMEePFFW6ZjWb0dY1ITTIrZrd\nUYbME7yE2jjWtWNdWdaVpfWwc2mfy9efY7h7ibqp+e7b73AyX8Y+HkDHV+t7CJuQhBDpNCUQaQzX\nfHrnDnfv3mE+O6Nal5jOI8U/ji7Zi8UWEUAJtAKC53Sx4OhkzjDVyLEEHXOKNNXkacJkMsRVLb5u\nUVIx2Z2yf3mXYZHGRr+UNNZxOFvw9of3+PHHD1k2TXzYxKFx5wNCekSQESCAp65r1usyUnzqngn+\nYo4cUVC+YyEUHR7OkZJPbhCSMeXJCU3lCD6qftnWI/EEG6vwsnueJoD3giTJ2Z4mtHmGkJL5ak1j\nA9YLWuewOHb3L3HdtZyeHrBsWhonuLR7iZ3966zKNfcf3GdVVoQQ4Yjet/i2xDVrXNvAeIdiVJAk\nw824lZQCb0LPqvpMxybM7YxCSMl0b5/RZMJqvM2niyVidZvdUU423CLPR8jlinJZ0TobZSramtOT\nEx48PGA+n1FgGYwicMa0LU21huCQQmGCxCWSvXyb6c4NlCqY7E1ikhUuiHT1SdcztlHgmWTnY+6W\npjkvvvQK//gP/glf/9o3yJLs3DuFruImJEqlTLZ32b10hfb0PloIRkVKYyzzszOMD0y2t0kKzXCQ\nkWUJfpAgVyW+rclchrWGsm2ZLdacrCqWq4qybFhVBj2cMt3djwCI8Yhf/uqbtM7zgx/+hLP5okPG\ndIB3Ip9QxLt0fbVuEa9XKx4+uM9yMaMq13Fm1EWQ/KZx3xNgd1XFOHQukLIruCCwTctaBiSaItWR\nKUEIhkVBOpqQIkmThGw4IBtGgijnLZXxHM+X/OD2Pb79k495eFYRImQIOAfsh3DewfY+KnfVVRTX\n6VFCwKZ3G59ZLKjE8NbTtI7GJly79VW2b73Bz955m9XyAevVikyCcjo+aeHxLiHtUF82QIomzyKs\nLdMFOmh83TKvDMl4TL53CVnEEHTnUkKQQ4rBFa5e/Qq//63YYjk4OeIP/+0f8e3vfj/KZySadVNT\nN3EW2LoWYyqsadja2icdjBAhYBtDXS0xpvq5dHNDT/OUxX4Ox+haGEqSqCHjm6+x//UlVdNgzx6S\n2RIMhKBpKkNdxny+rteUywXKlAwTixYCFzytiyJPdV3jnaN1YJyi1p7D9mPa//lfMb+74Nf+4PcZ\nXZoAEUPSn+HT1As+xRrwEgAAIABJREFU73gG3trYtE/TlJu3bjIejVivlohhRKwIITsURhQYTdKM\n4WSHye4ljpLYcM20Iksk67Lk7OiExim8zHEyYTQaM8wnZKMK18Ze5ursmJlpOSoNB/OK9WxFsAYh\nNaPxhGwwjKwMqeL6lcv82te/jm09f/Ojdzhbl3GhhZ5S8dw4I7SzLxtFqt1BljMdb+HqmpWPw7nB\ne4IXuA6HK2WvgRnDTK0iF8wwz9geD2idZbUuaaxmoOIwtBaaQucM0zzShyax+tg6y7xuOD5b8cH9\nI97+8D53j5c0kasZQTgPR71HOCI3DxD1M2ORp23bruXS9QK74hGc8/04Gzg9XfLo4CFWTnn5Ny7z\n/Fd+nb1rL/Lop99lcfwpqmzxadQtDRhUK2n1uehuGEiSYkyS5GiRINKGcTGAHLb29tCDCd5L2loh\nxYid7ZzxtZxBnsVKtG95/toVtoqCW5ev4pqW4daYH9/+gL/6wfdYVBXBNbSlxbYNbbViOByBCDRt\n1QFPVp+zPj/fSMXmkQtUNuLSG98gaMXDH/wFdn6IwKKUQOeQdyTpIcDIKYSQ6FaiW82q8VTrKFnY\nNIbWOGoHDsmqrFnc/YTFO4c0d+ds727z5j/8TZJhvilG9hb694oQitCvwHK55Nt/9W3ufHqXGzdu\n8OKLL/LCCy9w+fJlhsMhQsZkX6c5xQhG40ns53iLxJElCWmR4lcNpwePWK5XzHem7EymJBqwNcIL\nVquaR4czDs/OOD49Y366QBvDMJGxgCElITiE9KRJhk414prGfv3rLNcVb7/3Lk3d4gAvNjK6EDrS\ng+7GjIZDXnv5Zby1HDx6yJ1PP+V0NmNVlbRty3q9ZrZYRPZ2YsiuZCTETpSIA7ZaMR0N0alitqqZ\nl2taDyETaOWQKoZklpjvOtMyKxccnJ5xOltzeLpgvqg2wHTfGaEUEazvOikHKSWJUmR5xng8IS8G\n3Vxl2ITp/eiY84HVumGxNqyWNcfHp8zmc7Z2dkGPkMmQ/ZsvMJlOOPnkNqs77zFfH5GEEmmr2ITq\ncKJIwbquMEIy2dolc50GjhAMRkOUSjk6XLIUkZFf6rhxNaHBG4NKBFoLMpnxlZde4+buPuVshg0B\nLQU//MlPWJYNdE8p2IZqdUJTncVr8i4Sa1vztKX5xDo9d62PL/4LSCQBajDk0htfIykGHP/or/HH\nd9E6shCqRCGVAHzHR9ugnMXjWJUt80XJqqppTKTRcSKjdYJmYcnXgHfc++mH/MX/9m8Y7Ux45Ve/\nish0J8wVr/GiKt4XHc9knM4FrG159OABZyenfPjBB7z11ltsb2+zt7fHq6++xquvvsaly1cYFAPy\nPGNUFCjvccZACGghyNMUORLIsuTo9BF3jg54lGUkwiG9RSCjdsdizWy1oqrWJM4zziI37sGiotJL\ndq+USG9I9BCZF+hsgBAJZd0wWy748OOPsTZglUZyLtXuPJ2Ks2CYFzx/7RpaKq7u7vLCjeeYL5Y0\nbYQSHh0f8dYP3+LewwexbYIgUYJERRpMTSBRkiJNGI9yBkVOPtfMVxVr02JmMxZ5zaAoSJKUIKE2\nNU1dRw4hJXHBUbc1UvpIOxnicLiTxCpqVx3WRK2Y/cvXeO2NX2Lv8g10msUKdC92GGIlel3WfPTJ\nQcwHjcNYTxAFzksOHtzj6P59Ll+/Sj7Z5fIbY4Y711ncvU198BH+7B7CzBGixQaL8xaEZLYu2ZrO\nGegE6pL5cskoG3J6tiBxKrLca8Uoz0kkNALyPCUfZJAopPdI6dkZjciM4ccfvMe7777Lal0SQh/m\ndR4wuMiYtzGrXyxPi2v2caHhrvJADw1MsiG7L7yBqNcczA+ol3OSbBBZEruBdh8CVW2ZzUqO52sO\nz9YsyprKtjQEvEgJtcCvPMNGkzqFDJJmvebHf/Fdit1ttnZ3uP7686B65y1IkuSZJ1O+WMiowxL2\n4y6BSJrUHlkePHjAbDZDKc2NGzf4xje+xre+9Q944cbzDEcjkizHtDbynhIQSpLnCfs6Z6ADi2VF\nWa/wfcGpW0yJcUylY5THC1ZSsqgNB0cnyFXLpf09dnd3QRDH2QYZeTZEKM2iXFPXFYePDrvG/PlY\nWIT0xbAlSxP2tnco8pzd7e0oaWdt7O0RWM5OGanAj97xzNcrjA80xtA4hw+eNJHkhSZJI5NAlqck\nWjEeDWhdBOU76yl9gzQmhokSRqMBCslyvUIoh1CeySjDCkFdG2w4h6pJBKmQDFXC5Z0rvPbyl3j9\nS2+yu7vXMeuFTbvId1+3xjNbGEJPDRIUUgqq1Qnv/Pm/ZnX6iK/8xre49fqXKSZT0v2bTItLVJMX\nmN99l8XBB7j1fYI5AVvGHHxdcnJyFKMIY6gaw64NoIcM0hGJTkgThXEB5R25gB1bkEmBdYHQVGgt\nQQZMW/Hex7d56/33KNv253Kwi2HqY+JZf9ejj2+7+oFKc8ZXnmO1vc/pwQEnB8c0vqVtWuqyZjZb\ncHy64GS2Yl42rBpL7R2t9OA1qtaoUpOaBBUUhA0WjWZe8cG/+zY/vHmTyaX/nNGl6QYJ9YvIAD4T\nNWagD7XCBp/qO9l3ay2Hh0c8evSA+3dvkyuH+u3fYzSdsH35OtXqHqa1+DzBC4GUkKWKVBaMdELd\nNDTGxPK0i/le3RrWZU1TB4z3tD7OW3rnmJ+dce/ePfYv7zPYnpKFgE40eVags5xfC79M09R897vf\n5/T4FNsYvPWb3qXqIjalNIPBgPFwiLGWtm1ACIyLw8mZt7x6fZ8d/SXqds1iXXLv4IT7R2dY57ix\nt8V0KydJ09gP1JpUZxS5xQkR2yt9Ycd7vI+aKDKAaVscKTvbA16+sYP1kqr1HJ3OWZYVUidYPMoF\ntpOcbZWxP97mymSHTEh0iLywPkTaSk/UkIzVWoH3ish9589Z1kPD8ugjfvJXJzy68zGvf/13ePXr\nv87OzRfRSUFy6TnG+TZheovZw/epjj6kWdzHNjOEqxC+jEz8bUvdOFqn2J5eJt0eIPCoAI3xqA79\n0yaQJAVSGu48/IiDBw9iE34w4NHJMbNqHYWALtpP50H73u2GeuXCIn6WVsS5sffwv4t5X/d+XiDa\nlK3BPnL/KnX7KYuHp5yczlksS5arkvmqYlk1VNbReI+TkpSCpErI1inaRAJuj8cS+YKSIEm9xh0s\neOcP/5QrL97ky//pN0nHI4QQ5HmnkvAMxzMQfAl0L1ceIjRNdN5FK81gULBaLTHGsF4umZ0ekeUJ\nO7uXuXzjBic//IRyXTEYZCRCxOkQEYsWaSZROpCYSKsYTOgoNQStBK8kTgicNRhjSZOE0NQcHp1y\ncHTMZP8y+cSSEYV2JoOCV/OXYiWtrnnrrR9ydjKPilA+Nin60SUpBWmSdrtYp8RNQFhHUB5ZFOxf\n2mM6ANusqNcrbky3uDse4/C8eHOfnckYqRKQSRyJ0wqV6C5Hje/ZrSh8V0MNzlFrQdDwsr7G3nSC\ntYJ1ZXl0MuPwbI6XImqOesGlYsiWTJDFJI7BmYrE1QzTLSoD1gUa03J8ckxVV4gQkN4hgc1cSogV\n3KiYtuDgZ99nfvgR9z56izd+5T/m1hu/Qj7awyVT9N6AcbGHHr/A7MFtyuMPaOf3oDkGXxNsFEGu\nFnNc03SG4DcshnQtH4vFaw/Sce/gLj/4m+8znUy5duM5GtN0IBFxARvbNemJkMRn6wf+fG7Z3e7H\nQ+Fw/k9AIFzA3Zsx+7N3YXbG7rU9kgzyVKG0ihM61pIZT+U9SnsGISVzA7L1ENlohJMYAoYoCSIB\nHSSZ0KTEEcGT9z7he//7/81of4/rX32NbBjnofMsjYR17u/qOUMsauhuvEkotdmBlFKMR0OWywLn\nPR6JlymjnV3yyZg8S3B11DwZbw0odLaBMgUCXsRiR5pEjhUrPKaJnDlSSUI3aGxsbG+kUqBC4Gyx\n4vhswY26YWwMQXS8tKlmmk95/fXXOD4+4d69B5Rli/OhU1UOEScb4rKQXfsnTRK0ioBmKT3OGeR4\nzNWXX8fXC+r5KevT++yMB9zc20EoGIwLklSjggAcQqrIv9u9r+p4W+mmLXrJCCsVmZSoNGVQFFya\nTDAm0LSe63s7nC5XGO83QPA8SVEqgcEO+e41drZGbKUK6RrwCu8Fq8WMn91+j8Vi1i0/d2HRX0AO\n9YC20LBePeTDd844fniHB598yEtv/g47V19BZENUvk06zSjaAss2QV2lmn9MW94jhBmCEhMErXVY\nFxDCR5SskCgEWiUkRY5ME7IsYbp3mfHWlPF4FKGKp6cYF3PAnmE/dP+JZQF5joMOEMTTCkJ9OPyk\n9+Xxn4nNq+PXLhAezFn92x9x5w//DHd5ycuXnmOQ5+zt7eID6ESTpqfobI4oA+O2oFgNyZc5YpXQ\nmMA6WIywhE76UgdJiqIQCZlUOOFYmZoPvv8jwj//X7n1jTe4/ubLzKoZ0kX+4C+YtXk2CcC+YihE\nNyfYj/dIRT7UTKbbtMbggNIJvC4oVyXr2QxjIs1HWa4ZZIJcZZsb1T8A2TEUBO9xPbxLORAuytVb\niySgBaRasqwrZvMFLoBxntY6BiKGdwLBdHeXl155lel3vs+DB4fkRUFrTCeTft4Hi9SRsT1hraU1\nhhA8SaJJ0zFiPEaK64RLa07uKMpHH5HkgSST6DyNUn3G4FoX0UFCdn3VEIWAuia4EhLRqVlLCanO\nYj5mbVTbVg4tPVomZEkSqfw7Tx6Q+HTEzo2XyCeXGAxGkTfYtCQiJQcGOjAZ5SS6f5weetlzwhPr\nd3P1eLfm9NG7rObHPPz0XV76ym9z45VfZ7h7EycTbDokmV5nnG2Rji5RLa9jygOCWaAzqOWAxtrI\nUC9jbts/y0SmqKDIdMKVKzfY2t5muV5gWsejk1k3oyk31fMeONCnAr3hhS5P9L4HYKwvtMnOre/J\nam2fTrhuxraX6PPzivov3+fw//wOP/rxW5jGIR8kjBJYV2sWtaFyUDpPhSfIDOUz3CqnWmsa37KU\nLSthKHFYPBJQQaKDIsGigZKGWajJztY8+JMT8u9+m+LqLtU44fjB4TNR+D5DznnxoqNBCqWiglgX\n7k62t2ltw2K5YrUqqZcLmrqkGA0Z7F/BNDXluqYsdHxw6XlrdtMw7ryY1po09TTGxgXku8kFAYkS\nZImmxnXcRgVSJVR1y2AYSHKN1FFf5blbL3Dt+g1uf/AzhIjGt1otO87a8yJRTzdpjMHaFiTIkCBl\nlCG0XpCInOFwQrazQyhrECFqvmiFlwnG1XSiB/Q4AkEnP0g/cdFVXrVCJnrjKbAenMFbS7AeLSRS\naUQ2IB1uU4y3Ge9eZjiZUrUGoSRJGjGpBMikI93K+fIrz7M93eLuvQf0Qr1PscfNeo7bmAQc7fqA\n+x+cMjv4kEef/IQXvvS7TK6+RpYOKPJdhN8m2F1sc53ga5IkkGeOwhzj7VnfJIg5vYyqAN6YKF8f\nEoIInLWGH390F6cLTlZtxNr6fnysd2/n0g6P5ZzA0dEh//Jf/iu2tsa0rekw0OfoqCf7nc45jDE0\nTUPTNLRNE6OnZUX7ySHLe6cclnPCR54//9cHZFLRGEPdGuqmpaxqatPgnMdbCQ0IF9tbBochYLkw\nVBEEMkRoqBUeKwweh/ASGRSiknACXkHt26719Xc0zsf4TkI/ihTxjzG0UKRpxnS6S0AyPz3h7u0P\nuLI75ZWv/TKXr1zhzg/fwjSnNI2hSVsQKVJEzpoohNPJCQgRUS+dcpn3kfE80Zok0WSpJzOeNAiG\noxH5YEieFx1pcWRtSDo6irTIGQyHMYfwnqIoNqrOfcGhF5bxPiqWBQKJ0jgXZSUIAq1TUp2QTHYQ\nmaddrmitQxcZQSrCeoFqHcrHiR2pNb3MQ3CRcUCL2NsSUiCStFs8FovEGI+pG1xHmqV0xtZ0l93r\nL5FNrxCSAXmeE7wFVaK0ohgUm5zSOUh9yt7WiDx52uO8sICJ1WpBXEyhSy8QAVzN+uRnfLQ84Pjh\n+zz30m9x9YVfZXzpBZJiRFJsMxyO0YlAZZosaRFnEnd8Bj7ETVcrVAgIH4fjV7MFppGU1Zqz1vL+\n0ZLazKmdIM2HWGuw1nYY4V7fRmzW3WYiB7h79x7/4l/8T49VOb8oL92grHx43Nid7yr5Ag497x+f\ndBuC73Jhun+jAfVupFcp34Dxus2331Bistyd90Valj6f9v2eGfpo/nOPZyKVVl3TtDdUrRRCakIn\nYSWQFIMxWmecnpzyve99nz/4g3/M9Rdf4rlX3qQYTLn3gz8lhDXWBZR10ThdJ00fekluSWykRwSM\n975Tzw4kiSbPPLnxyLZlXdYIISmyAqmTTkC2K1YJODo+5uHBI5x3SCVI0kiF35oGjNsYZq/74p1H\nSY3WGc41XetDRPWpIkcNbhLMDnKrYqgUMk1ovKc+eYBdr5C2iax8vYH4qMZtvUepyFCohMbLAm8N\nbVlSVi1V1eCsJQSBLMZMLj/H1VsvMNm9QuMUtYnwSSQMBgMQcQRNdqBNoTQazSDPY6srxKUjuMgu\n6DuSMJBSR01Ucb4Ko/MSiADWzDh++BaLs4ccP/iQ5179DS7d+gr5+DI+zQhEPRstNEkyIgy2kIUk\nGxWkMqDKNb6tKNuKtmlJW83hes2jszlzE3PUIBUiyUl0irIWaxqcNV0l59z7CdEBMYhTRPP5/ItX\n9C96uO7v3/IIj30VnvY/Nt+4n//R5x7P1OesmgaaFqESEmPJCo9SnhCiMbWmxjpPqhOMjTC61978\nJdLtfRKd4YdTVrJgPj+jqA1aguwoNEIn49fnfs45lssVs/kKY2POVbU2lu+9iJMxznP3wSPuPDiA\ntAAhSbOcYjAjyTJ8gG9/57t88MHtKHbbGXwQQKd6XdU19x8+ZLlaYYyhqiqkikiRtm2BCEfUWm9Q\nHUFEOKNWjkAdqRHXhmplSOuSfF3HvE9Igo/jdr7r71rnab1GjQKKwOr0lLqtCSGOcTUhpdiakBa7\n3F1YHpUH4CP5mOwb497hnCFNNWLD6hDz/5PTM+q6geDxtsUhuhG5c+JsECjpQfmIAnpiMXkfCMIR\nwppydYe7H52xnH3CrcU32b/1dfLhHkmaopOEVeoZZismkzFyrGmlx6wWhMUpvlzStHNCqJCZ4pOj\nUz6+dw/TtvQ5Zf+hQsZ7LgVYY6Jg8AUPSvB95vn/u0N8XmgghAhpkpBl6abKKjvVrE2lTLAZTpYy\nMiYAXLl6ha3JdpwsaBvWsxO8qenocboOVOgACt3PxLnmZxzdiiGB78MSwPoQETtSsbe/T1EUXR82\n4l6ljONdp2dnnByfRAgYXa2he2/jHNPJlFs3bnTeJlyY4YzhzSZQkXED6rLI7pIvhIreEpoS4W3H\nf3Mer/hOqEXQkWgFgUwyBGDbOn5O93KHROcDVBLZDTdznJuoQmzws/3o2sXKpDGGT+7cZbFaI6Xa\nhF2bZR3YnIvoGRQuHps4LZy/GIGUKflwm3w4Remsa6VJhAho7ckSQaJjLTq0bfzrLN4bQoiyiWXb\ncjpfYqzn4gc/xsLXefiLP+tPK3j39wdG+P/gEcLTg9wvNM69vV0u7e8yX8zx3pMXA5oq0mTobnpB\nSEldrXHWk+g05lZC0NYNEkHdtjSm4er+Ls9fv0ra5WWNMdy594gHR8ddP1VtPJzrSu09t6sUoiuC\niI1y9nmnjC781XEo2bmIOPI+FmSUIM0Kbt56melkm5/+5IcUqeZLr79CmiSIJ26NOL8BPCm/8/h3\nT7nRT75HH+4IQegkBntCa9kt9LhJ+U2I+WT9ph+sFgiQCqWjKlu/YIUQ1E3DW2+/w+HRcU/uEX93\n00roilNdCyL0Xjd+AEJKsnzAYDSmGE3Jh2N0kiHU+Ua8ycVCb8uPVZh+7kZ0+/mFG7uZDepv72ZD\niwwJF0622yyMMdz92XvU1YrrN54jSdPPufsdW5+1nBwfMTs7iZXaJ191oY7yWetfiLimpts7TCZT\nlNLdpvb0p99XlT/v2LS2QpwwOjo8YD47+0zj/HwOISF48aUX+PXf+hU+uP0erWnZ2dnn7OgMbyyj\nrS28BJUoTo8OaGvDoBgjhALhOTs8Jg2ao8WMeTnjN/+jN/ln/8m3mAwKnDWczuf8H//Xn3D213NU\nohkkKbU1BCmpqhahJDIJuNCQaEWeDQhBUZUN+EjfCBHpXwyGDIohwUSRnrKuaNoWKSM58u7+Ff7R\nP/0veP21N/kf/4f/nv1pwX/9X/2XTKcTlJSdR4i42di7FDgRd/Po1TsyYB73jtC3AaIhBOh4Y4AQ\nVbY9Aak1WmWA6Ly5QCdJnCwBgjNxvtLG1pHrFlUsasRrFQhUMmAw2SEbFN3YWjyfg8Mj/pv/9r/j\n5OiY5MKJ9eckQ9d/Jf5LN2kjiATQqtP8vHrrFW6+8iaXb77CYGsXleYIFYfJe9U2dxEWubkDj9+T\nTfU0LiTAI+Tj5F1KxBlbJQWqkzN9jHUvBNbLOf/mf/nnHD/6hN/+vd9nOt3+vBUbN6q65K3vfYcf\n/uD7NPWaXuGsP/o0pYemPmmg/Tnkec5LL73El778S2T5CKXT2Kl4Mp8U4emGfjFyufB9CIG2afjr\n//CnLOazz7yaL+at7RjSQ/AorQlEjUjdoWu8ikWTwbAg6Wj8I6mUQSlJ0n2E1prpZML2dItRmuCs\nYV2u8V2VtL9hfWiJiMRiyCiSK1RPNNapmj1R2VOqQzG5C2FRV1wQUpAXBbu7e2x1u6BzjrppaNuW\nNE3JkoQsT0m1JumqxeumZtn11cbDAYMiJ+2GkcWFxbgZqBV9d8RhrKVpWtrWgtJkxYjRcNhFDfG8\ndJcyJFpvNgfbxrnCuipxpsV7iwseEaJBo3KKrS1G27tkeb45B4smzTKUkORSxiJQOCfR7j2e7SOO\nEJBedAPoopvltIwGAy7t7TGdTFH5EJFmqCQjklUJnBcd3ejFUojYVMA3P/s5j9STRbPxMFIQ6Vg6\nSpaoNRyHrCMKLb5UJSlSSvK8oBgMP3e9xoKlZDQao7WivRD7hAuv+aK5StGla8F7Eh0VqZXOEPpx\n4xTdznwRw91/xpP34GKVWUkVJRw/5/hC4zw5Oeb9Dz7g4PCQ0WiElAnr9Tr241KNytI4XVHVmMai\n5PmCSZIUTYIPEeo3HBToC4j8pm0py3KTY21O/uKVB+h3xB5ZFLqL71/Y90j7C+9L5nFBRIPPsqyj\nJexzNMvZbEFZ1TjnSJKE7Z0p+7u7jAcRb3t4csb7H/0M7z2vvvQ8N65eJkvTjfRhX4zpjdOHCKVb\nrtecLBZUVYOSivFwCEkKJr1wbeC9pXUNTidxAeoEoRLSwRipNLatMHVFa9vYAgjQGEN1esZ83ZCk\nWVxEIXDw6IC6qkmkYJppBHEe1AaP9QHrQ9R02eze8feU0p1quWRY5OxuT5iMI82Kcwbpkqh924cG\nsMnF4wLskXsXnt2FRfhz85bd8wyhI/MKsegXiO8jg4/0LJ1XD0/Lj/vjolPqIus+XYjGpJ+aIjw5\ntfLk0V+Xc462bePAPV0V/DNC12cBsj/+C1/8ks9n3wuBO3fvcTI/AuEZjYckyQFN2YIHmSTINCHP\nU3zbUJcNSTon0bH65o0jVynOGrJMM8hTVJSTJhDJqsqmod9VPbGAEkJkQBMdU7bYGEP/MOkM9Txs\n6rmBeoZz33HVRpuO+VRRDLscD4oi58qVfZSSnJyeMl/M8MEyLgqGeRySrRvDwfEp1hpuXL9KLJDE\nz3HO0xpDVdXd+0UG9tlizsNHjzhbrhmNhlze3WVna8SwiNcufA9Fi3hbb8GIfiBcEqRGJXFAOxuO\nY8GqWuFMEwtjBJq2wTYGhIybGoHZfIYxsRI+TqIapw0do3yIbIQ2hIh5CNFYEZK8KGKEkyiKPKFc\nLzg+PmQUUtKRIk2KbuxOdB6fc3y0iBYhQ4iCUZs/F1df77OeXI0xZw7E+kIIAh8kMsThACkj/tl5\neOqaf8rP+nUhpCLvRvXiCf/8i7/Iw8UWW6BpWoxpux34/x1l7s86vtBzGttSVnHnqOsGpdQFhxa5\nZpQUKC8wrcP6UySSRCqyNGUwKAgYRvkYEeDw4DhSPDYtH/zsLmedope1Fksv2dcZFXGWFBlZDGI/\nsmsEd6Et+M5zqigw5AObEsAGoqPI8xF5Mdwk9HmWcnVvh8FwwN72FvPlAoFgOMhIEwH2HPzgXcTl\n1o3FmiVnZzMePnrEwdExh8cneO+5evUyu7u7GGNYLpdsjcdMxxN2dnYYD4qOgf1i7tMtds69PMHh\nusa89QOKokBlA5K+V2ktiAiYWNcmqnZ3a2+1WmKdRSvJVpFGao3OY1oXtWVcoDPW6EVlklIMxwQF\nLhiaesXJyRHb12q2VIJKIqCjN8zQO88Ls8KbSFWcE4yFzoA3vxMez9NDuNjcj///onyn7w00gHWd\nStxTjs3v9//23lNEReskzZ7yqqeF3E8/QojTUMa09DOzfTT2C3nJ/nx/Qe/6hcaplSTLdISymdjM\n1qlCqI6hznuC626u7KBq3aKRUoC3yOBItOThwSkP7x1wfHLKumo4Pj3j6HiO99A2cQPoPaMQAmc8\n1jkQHuEifWJwHQmxjgglfJzc98F3yJ6OPU92cmvds8nzjDzP6X+gEORKM85zBmnKdDyOoGcdCZ1d\nsJuJ9bP5jLd+9GN+8u77rJYrHj465OHBAYvVmroxOO8Y5BnbO9tMp1MGgwF721OOTk65dniFS7s7\n7O1MmW6NybO0a8/03LLnSWE/0ua9o6lWOO8phiN0OkA7h3dr8BbT1CwWS4xzm3NcrhYdBhlyCakS\n+J5HiNjn3Rirj8gZ2XMFEb1pvV4zP5vRtpbheEoynOKlihzAoePxJaqpAZu2x4bMihj6+X7zlPG6\nZP8QOicajS3yO130arGlBUL4TXsretVfzBAuzk1+lufsX/d5RzTOhqqu473VkvOkTGzyzc87j6ed\ne9/H/aLI9hlqXQmRAAAgAElEQVRACAFru9K/6os0XcXOR3Uqval0RuPUUlPoAi00zliChfWi4u0f\nvUtdN5R1jQ9RPdghSdOcDVLHRgb3AATvorcJsb+phEMpSao1QqkYmkm/2eGCDJ1x+kg2liadpJ0n\ny3KyLMPaTaLawRAlQklUkmzEjoJ3zBcLPrx9m/fef5dP792jbWzc4Z3vMJstjY0IoBAEZ/MV9w9P\nGA6GDIdD0jQhTTWj4YBhkbO/t8drL7/Im2+8wfM3rjLMk06B6tybhi6cU8HTNg2r1tGiGWUpUiQR\nleVqqvWK4+NDkJKiGCAEtG2UDZQikIpAij8nOwNCN4IXgoxJhQChM1qhKb2jNpa2abHzOaenM2oT\noi5Ip1oeNjonIvLVivPFLYKPf/vUBIA+FenOoC/09e2iILr8TTy2vi8WTeKacJ/Z4nxasNy9I2ma\nkqbZprDUG8qTBvM0A+03vBACi8WS2x9+yGpdsnvpMteu3+raORd/7zxE/jyDf+zzRX+mn318oXFa\n4wlri0okSRp7XtGDxnJ48AGvBSgRK48qwbWuI3ru4yBN0zjOFmu01uTFCOs91pdkaWzQRyxrhPV5\nYSNShDjRgVCRYCsQN4LOUwbvY6NfBqw3XQGhVxXrbpkUJFnCeLxNlg+x61X/VECqTlczbiwBQVm3\nfHLnE/7yO9/h+2//iKOz+QYQIaUizVKKvKAtLFVdU1YRHRUEGOdYrte01pMXOUmimK9Kgne8d/sT\nvv/OT/nyT97j97/5G3ztjVfZmY6jQC7xNnkZz0GFgBIB09aY9RIRRuQSkAqEoO1CZ6mTTXGqaeqI\nRRaSTKuuYhvD103VNoTO0GLo5yVYAaaOMELrQTpHWVdUbRvlFpGEjnBMYBFYCJbgfG/2HU+U7/qW\nElSKJ8I7BecLUYTYJ+xz1/j1ppLz1MUcwhPtiCdfc+GrntC/HzqI9QEJ4ufbJcAGkXbxEEJsjNM5\nx2q14vbtD7l77y7Xrj+H1glXr11HKv1YwfKLjouG+Vg/93OOZwK+p2lCkiUgAy64qEXj+6KAwvnI\nZZNYSHSKlh4ZBImKc5I9xUkELcSvjTWQeUQhY+/MWeqqphFR8gAbuqmDeDGxTyfRHS7Ud+G06OT6\n4kV3PycQuhsgZexXTaa7pFnBer187Nr6ECMA8+WKt3/0Dn/yH/6cjz75hNq0aJWgZNJJ/cWqsxAS\n5yNLnpQCjUAlCu0lxnmsMxASkljmxPpouKezGd/5wVs8fPiQn37tTX7la2/yxksvsLu1FRdEt06R\nsUCjjKNpa2ql0GkSK+Q63sckTWPxq4tW+lw6wuFyEiVjNdZH8mq6YlLwsffaek/TNpTesFqvIwl1\nVqCzgqwokInEd4W7zSpyFteuacoF7XqJcoZERG/vbUvT1KBSiq09dDpCpQUqy0AnBJnQmc0mPxUb\nw7zgwcQXh3tPX9h9+BowTc3DB/c5PT3d0Jw+6S17I7zo6S6CE/qvnbM4Z2mbmjvGoLWmqUuuXr/B\nYDjqoq2nn/HPcRldNNBnuMpn6HPKaJypxgZL8CKOPHnRLVRBU9dRrsEZWgzDfECeZOAipE91u7hp\noq5IvzMVacZoNIjkyEJQlSXL1YrlasWqLGn9edgYsaAOKS1ZlqKlonVd105EEdvHJhq6Ki1EJvrx\neBzlC7s8YTNWJWJ7/2y+5K+++x3++N//e+7ev4fUcVORdApdTRvpQELUylytVjSmRUhJnmco2VU0\nhWRdVjRVSa7j7upaEzVGraW2lh+ezfnwzh2+9847/MPf+nW+9Ru/yXNXL3ck0RCEREsVJSqqitZ4\n5LBgmIrzjU4nuBCnaISUHYJFxApukiMThQx0NJ8uCvuGaJwyOKyJhae68VhjIkKoGDDa2WN3/zJF\nlpL4hhBafHDYZkWzOGJ9dsB6fkK7XqK9ZVTkaCmYnZ0wm52RFmOme1cYDKYkgzHkI7KtHQbTPWQ6\nwCNxnTfuBwcfM5Anvn+yp/xZR1+1D95yeHTAu+/9lNPT440q+JOHlHE80Vr7mLH2cM4nw98QAlVZ\ncvuD9zg7PeFr3/hlXn/jyyRp3nn38/P+rDzz875/2vFM85zeW4zxCEWcUJEK4aOIi7UxnCyynESl\n5GlOmuVdnuhIlcQJqOuaqirx1sU2QZqSKIVtG4LtFJhSTbI1pkhT0iTlbLHENg2BLrwK0NQGIWKu\nZbWnbWtEEIgQh5dDd+MRdAiYKBK7tbUVkUB9CZ0YyiJhuS759ve+xx/+0R/x6b27jMcjBoNhLH5J\nTdtayqrBI2itpV6vWa5WuBBH0fIkBeKYmdISUWS0jUHL6GEJoJXq+poG6xxnpzXvrNes5wtcbfjP\nvvW73Lh6uSNRiwWr4Bzr1YrWrZBuTLI1iEUSY2nWcXxMD0dIBAldETXE5xXJ5uKmFqTePOoQAt60\n+M44nfNIJREqIcsHXL1+k+s3bpJrgVkfUs1PWC3PsNUcW57RlgsIniQInLWU7QpTrykXM1QIpKki\nrI6pqhnlLMHrDJENGe9fZWv/OtlwB/QQJ9ILK6xfsBtLOPegXdn+SaPdLHLOYfEheJaLOR988C73\n79/FdrIV4in5Xd/bzrJs85E9+KEfwrhotP1oW1OVHB485P13f8pkMuHajZso9Tik8Mnw9W9T2YVn\nDGsJHtvGxSekJE0j23tbG5QQDPKUTKdolUR2cKWwxhKEwHiHaSqauiGEQJJEtEWvU2iMobEx7ped\nV1BSkWUpWRa9dZ/fJmlCkmhcx2c6GY5olMLh8SJ0VI6AikWeJJEQFFk2Yrw16RjSe+RphLFZY3j3\nvXf5d3/yx3x891PSPGMwHhF8wDjXvV4RfMzr+hm/JCvQzpEJSdH1WVsXPZRSUAxS8lRhvccbT2sN\neBursULEto8NHB2f8Vff/xuuXNplMhqyM5nQBz3O2Cjq2lqGmcINU/rNsirXUdBYBrSCVPX1loC3\nBn8BKufjThXDYgJGQBui1J8SsXhikwHF1i7Xbz7PVpGwevgRR/c/oTx7iKtnpMKxNRwyUBrT9Ujr\n4LB1A23DIE0RMrJZBFPjjY90K2lG4tcsHixYnT1i7+qLjPdvIbItgkhiRVnEwXEVwjkuuA8t4TML\nomLzB8CzXC356Y9/xPvvvktVluevEhsbj564q4gbY0jTjCxXUWGtG7rvq73O+6iETqCqqiguTCw6\n3rt3j+QHP0AnOVeuXX+iTfbFx7O89pnY9wjgrcfaOJuY5AqCRKaKREmUjFVdoaOmR9sRPzlnI0ZW\nCAaDIXmWxaHcJEFrTWsMdVOzLktEgEFeMCnizKKoSgZpRpYkNMZSNS0Ez3g0AgFN3aAIDPMCFzxN\nsNSdQpdQsVKbZIrgPHkxZDgcP7GDCqyHu/cf8hd/+ZfcvXs36oRIxdnZrJvvVGjhaFvPcl1SNyVa\nCQZpzlaaME5zpqliXGi8CKzrQO08QicUeR6Hj6UgbGWU6zVlVbMoYVbWrBEEKbDGcufePX7wzju8\n8crLbI23uhAZjLUslkvKpsXubsWMTfQamgbpRNwsuqro+WN3eHfufXpy7RAH9bqpn4C1gNagU5LB\niGvP3WJvMmJ2731O7n+CXc8YJzAsPFoEUu0JCaybFoIgTwVOCVySYTvWRKEgTzv0UGtpzRpkhB+2\n1nJkY7tk68pNdD7GiQxLv7P03vN8xO1puNfN1xeM04fA8dEh7737LrPT042iXP/MtY7D4DLEvnhr\nogKYUpK8iFXd4aB4rB1nXUBkoqO3iZuF6+hTrbU8Ojjg6PiIS5evoHVX2PtbesmnHc/EIeT6qmiI\nN0QJidYJBo+xLS7AII2ioE3TbG5i27YI0cH4Ek2WF0ipqOsaV7eETmVrXVf4DusaCN28omBrvAUh\nsFivMNZ1FJYtu7u7DLI8Fo2cI0iBkrFgVbVNF6LEPMy6ltFoxGg03JTVIfb1Dk/P+N7f/A0ffHAb\nKRRKEmk8fcT6CmKhy1hD29akwnNpMGQnTbk8ynlue8yVrSFbRU6apDQelo2hbC06S0lzTZGlDIuc\n4DzVuuZ4NuPO0RG3j2Z8craMlW4Btz/+mHc/vM2Vy1fYGg0w3lI1Naez0yhoFCxKnnsW2y0S30lH\n9JMrSgkGeUKuxGaUz3kXN9gg8UiMD1TGsWw8rfWIAm5evsT1/Smrhx+xPLxD4iomqWYrzxjnKXmW\nkWSRX7dxGikUWkU4Ylk3LMuSqgYXBM6WlG0bIw+lsW2Iw9RqAEExP7yHVJLJ/nV0ugVS4tGxptPV\ndXxXcfXhnCXjyVZFBO2Lzd7UmpamaTrD7Is65xGFVgotdbcmk445I7IvtiYit6bTKUII1us18/kS\nqTVZllEURRyoKFe0rcETIaBlWWKMPc/5ebwddNGOnrCsv7txRlROjMeV1JF8S8S2srFxNrLQsQrb\nXiAJNl1lK8/zGE7IWHFclxWtsWQdsa4PnkaneCx5FlnM100FIfL0JEmCbhKyNCWEwHKxRCnFpZ1d\n8mFCU9VR7sAbGikRhEgO3cpoWtYxGo2iZER/84DD42P++M/+jB//+CccnUbCqdZFdeKeUaBuWoZF\nziBXiFZyqRjz/PaU61sjnr805ebuhJ3RiCLLkUpHrCgCGyJKSahI4aKShBAExvj/h7T3aLYrS8/0\nnmW3O/Y6mERmIjNJVhXJbrJbTUZoIoUmCoX0SzTQSH9BAw31C3qq0FSKUMiENFHIdHSTLVFNU6xK\nCyBhrj1m2+V6sPa9QBWLldWsG5GJvIkAcHDO/pb5vvd9XobuwOXdNT9/fcn/+dOv+entAYzidrfn\n//i//wXWGP69P/5HSCW52+85HPZs1mvqwlJoTYoxj31mRwXwME4R81HdKEFp9dwIzV3vPNCH3ifG\nKbCfPPuQX+Ozs3M+vTjB3byiP1xTM7KuC07XDWebNatmRVnVqDJH9CHys3AfGTGMc+p413Nse/bH\nFp0C+2mk6zuEMSRbEURChMQoJHudFVjNqckNxllLG9N73yqkB+bTXI35h/sC/bCRHAKHw4EQ/AeL\n8MMlNjtvUqK2lsJkllUUgojEpzwDnsaBQWuMtZRlSd8PjOOIMYa6qtFakkQk0TEME36amGb56X0B\n/rI75e8r1N9kh/3hOycSIbI42swi6RAiU4gEEfPRTcjMZUk8dLu01qxXG0pjGIcRbSzDOODcQFWW\nlEVJcAGfJIuyzph8JXOojNCUxiCRKBRWaaxSYCztMHC326NlVvd4HzJTR0va7pjTthL4YWQ6+pw3\nmQRD33I87HNTKgS+efkd/9P/2tJ2/fxA5EG9VAKFxEc4di3T0PO4KfjsdMWn6yXPthuePTrl6dkJ\nm7qmMjYfQyUPRmRmsbaYGy1RKEKSxEKyqCrKuqKuG6RS+J9+xc9uW47O8xd/+Vd0Y480hqdPzrm6\nfofV8PTilO1ySWkKJudmM7Ug+PDBnG0eHQmBURot8hUjnw4lSku0hkkl+n6iE5Jyu6VZLrg4XSL7\nW7w7Uiu4WDU8Otlysd2yXiwxRYUwBcJatLHzEU5kMmII1FXBsqkZp4m269jtDiwPR+6M5u31FXf7\nO4KZsA1oKQhtpBeRoy0oqiVqZWfTvcz/pHkLhV+gJf5yU+heySuQBJ/o2hbn3Ac/n/9llOB8UfDp\n2QlniwVGCEJ0HEbHbpw4TJ5FVRERdMc9uxBz3AWB6AbGsSP5iSjB6ERTSpKLhDAxDgPBh79ThH/f\nj++Lkx/cPH8jhpBR2cgshSR4n+VcKpPmRMqKIKttxmPO0Q1VVdFUFUoIRHwvTVs1NUIqbGGYhCA5\nzaapqJRChsiisDw6zQ/F3f7Au7s7NIkUXL4zKJnN20MPM/KwrEo2mzXd2HMcM1g5zVI/F0b++i//\ngv/uv/3nrDan9G3P9dU7jm3L23cJIVX+IGabVXCJwWWGUfCOSpd8umn4/cenfLJe8fjkhNOTE5aL\nBaU2aCkfur5yFt/nXMgcPYfIKNEYBUFKvLLUNCQh+AOlaWPi5l//DV/f7WlHEC9e8v/82Z/xk9/9\nDOcmnj55xCcfPc1Foi3OB5TSaG3y8Sq+h03df9ZK5JzU+5tomI+HUwwMIbGfBM3ZUz7+/DnCt1T+\niJpaChk4Wy55erbhYr1kWVlKrVFagTEIbdHa5u/n2aGKEWankRBz2JOQWKVQCNw0Mg4D++5IBMp5\nHNS3N9y+zQlmG1uiigVRWu5R2PmvNA/5f+khfu9eer91CpHT3R6KclYmaSk4bSr+4NkFf/LFJzw/\nP8FKwTSN3HUDb3ZHXt/s0PUSUy/5/uqG11e33B1blIpslyUpBvr5NLewFl0UXBG47dNs7A+/dsf8\nVcL6dH9c/22KUwlJYfLxcnQTLmY+qxL56CdkVgZpZTI20LkZZwkpBUICYxWLouJ8tST6kX4YM/Sq\nKvndp08422yotWFV16yrgm1VooXkdrfnyxcv+PbyLUut2E8ju7Fn1+c4hbIoSMwWoaJktVpx0+4Z\nR49AYAuDm0Zev/6Wu//9koTAT4HD3QGr80zLWItQGiEVk5sYxongPdE7tmXBTz5+zB998RFfnG95\nul6zXqyoyiYT+YzJ45m5O5wXopi5SHygnZ2PZy46Usq7hzWW7WbF73/6jG9eX/Hd5Q03nSO8veJf\n/fmfk1zPH//jf8znn3zK+ekpZVnOx9c8nyuLEjezeJUUOe16Pur2/YAOYca2SKTKn1dIcNd7ol3z\n/Hd/wmZVMN4eWRlFGT2b5YKLsxO26wXF3OyzWqONRVgL2uTTAfnoKVOaCbk5FUAqDSFgtKa0BavF\nItPvlUZd39JPI2O3p9IKazRdv+f27bdUdU19avAY/APh7hdHKb/yK8/Y8oKY4ixhnG2CKQcC10bz\n6cWKP/3Rc/70s4+5WNaojC4kKcOE5jh4nNBIW3GzP/DVy5d8/c23pLHn+dPHLOuSfbtjcgMiRo6T\n56++v+Gv3x7RWmXm8TTNppBfFB38qqJNaQ43/oGj7W9wrM0roo+BEEN+EOddRkgxN3tMtpDNQ9z7\nAo2zOkVbS6ElRiRCcCxJVMrw6MlT/ukf/zGPHz2iMBolYDzuSX1Lcp5KgPKPqGTi0bLhZhz4+voy\nRxXokvVq8/A627YlhYCaZ0yZFZuwNj/Q0TmCD4wz81Rak1daIRiGTPLzITANIyEFFtbw0cUpP/rs\nU754/pSPNkvWZUmlCowq5oaCQiqN1AqpNYqUI+O8z+4ZmHWnuTOYs0uyTkbKvEecbWt+9Olj/tXX\n33H1emCIE7e3O9q24/zklIvTs9kHK7MkTYDRWbM7jj1D35PmUNfcuBMME+iUkDIgxAzIEom9S4zR\n8uz577Fdb+hvvmOpE4VUCK+p6yVFWSGkRimDnqmG1pYIU5CkzmOZmLK6hxxslf+MfMfPgOVsUtBC\nsGya+T2yvLu95dAemeIty3PLerHE+wP93RvK5pRY1niRmz/yg13x1z2b99iJECNDP+Y76vzLtBCs\nS8Nn5xu+eHTKtjQUSqELg9QCU9bYZoUuFoT5rhuT5w+fn3Hzk2e4tmVTV9SVZXQ9XXfkeLvj5dWe\nXTvx1VXHOIzs7u5QUlFW5S/E+/1ycX74fYjxFwKP/0HFGUi4FLJcT0q0EMSUGye2KKjKmrpe0B3a\nbHWah7UZiZPvn3iP645MyXNaljx/8oy6WaKbJeerBZumQhuLd2O2jc1HtdJqzrcrlEpc+Il3xwPH\nqeeuG3Fksnhd5btO17b4ccQqjdE6u+pDyHpRyNI+ElKkhwVHSIHz+cHOAUC5ja60YrOo+eTJBY8f\nnbFdr2kWVT7aR/VQjFLnbA0xA8/Ewwcxa1jTB8wcIUDNtLmo0ClgYqI0ksdnSx6tK758fYPzCWK+\nay/qmtpmlsSMF0MJwaqq+J1PP+biZIu1WSJpdW6a+SQ4RkMMChlzMeu5KdRGydmjjzl7+pSrqxfI\n7pZiITl0BwyS4AMiRpTM3UxtLEJrEgo50/5JCcEMEJPhgQkrYkTEgAgBETwqBhRxDv7NEsrVakE7\ntNzeXTElx6NSsVqeEIY9XXdA2FOCFA/vl/wVZ9oP8Se/6OEN8wOfNwSJoFCSi2XD082axkj81NOn\nSCkaDBLvBuSUrwhKGkR0mOQ4qQ3r8hF+GpApa77VGIhOMQpBmRKVhMJqqrKiMAVKqLxgxfkunO4z\nU/mVBRrntLrfqjhTyt3P+13x/o0hZW1pXdVYYznE/fvfVGv0PMuUPtLEyHld8GS15rxZ8PzRI1RR\ncTOM9F2XVSo6z+Gk0qALEg5tExUFZ3LF6CeCiKyamrI4MPaO9thSFjmGQCtF0Bqj9KyUyYIJmTKS\nRDBDv0KYO5gxd+Mmh3MB5wNxtmBZo6mMobEKTUDPu4C8b+WLhFB8ELSa5p2DDyLr52Qzqd4nYs/d\nVBESPggCCisMi6riZLVAK5lHGw/vq8nA6/v3X0q00WzXK6qywn+AkXx3dU1TVXihOKgKLyUyhbyD\npURMEhZrHj37hK7b0d294sQ4htZx7A4UwuKHEQVYJbE64ziRkiQ/IA+KHMtBnO3hWiH9nLOV7kFl\nswheSEg+9yn8iCBgjSREx+3tJcZKPjIKXSwZxxYdHUmah3t7btTdL0vvm7bMT2EuUBAy4OPI5HqI\nPp9OpKA0kpPKsjB5vulTQPgRFXSm+iMIYcS5AWMlIgbCNGRmk9Joo0nB4ydHCokYcjhxXpAitrCc\nn53NADCV///ME4rEH9w5f+kv9O9enAIeBrMPsiaymt+PIyImhq7PnUPyjmStRUoIfmBjLY/Kgk2R\nU8C2m1VOEJYJgud4PBIQWG1yr86WiCCJciSOGYmoRD4mWQHLssQAfXtkHEastaxWK6qmxhNwO4fr\nR6LMmSdaGgQ5ECmJ3HkW5MjBvhtxcxiPm18/QpBCJIwjU3tE+QlLJrorYWZZF7koyW6VfFTNC4u0\nRX4vhIBZUQLiYWV1wZOizy4bW+HHAY+krGrKwjKRi7EsS6RSjMPw8GGaeZcsqpKizAhNUj5l9EOP\n1oqoDGO5JEmBDCMqeoxUmGrJ9snHJGu5fv0VJnQoGemGgXHySJEVSWrW9ep7/KkS+ZRgNEJnNWzW\nEGf1lJTz4TbFPMu8H2KI9+4O7z1jPzAOPd4nYlKM3rG727FaXHO+foSQE8SOlO6zKzNfOM7Ewl/+\nSrNWS6QI0VMkz0pDrTRt9AiRqIxiW1qWJjcSo5RECYhcfEJDkgmfPCSHkPlY68cRtEEV5j19cLYz\nRhJJgk8xmxA+oAHea5fvDQMxvt85gYf5a/aq/v1WuN+4OJXWGG1mLq3OK/8sZFdCEVygaztC8Lk5\nIkQeiQRPmgbKRZVXeiVwKSLKguXpKVXVUCxHWh9o+4FysUaaElVGwIDSEHPcXJw8kx8IIVEbw9Jq\n8BP9rDC6KM5ICo5D+xBYJLTEZ+X3nIeikcrgpWcc8y4ZnCMEsv6V9yeCGAIyONZWcb6oqHR+Lbn7\nMatsYiT43JHVmamRZXkPbnmRtZohEIPPwoGY6P3E7TRyNzj2g+P6eODt7pZumuZIg+wCWq+WuHHi\ndrdDKpXvfoWlqirqusbavMPEkAUiD0ckW5FOPiFoRXADzo+YwmYXxWrJu1dfE6eOk9LC0DH2ETeB\n1oIQBFIapMze0dwo0yij0dZkuFXMdIqMRwl57CGyvlW66aGO8kKe8CEyDhNT75iGwDRGfFCMLtAN\ngdvbO5bbS5bLc3B7kqpIQmU55v1CkKuRlD4YQ8wfmEgRHR2L4PnJyZbu9IS/vbwh4jlZlHz29IIn\nZxuMms0QWhBlIs7iaqVkNk3o7HpKQSHj/HdXOlP/I4xtj1QapTVOSiYkqqiw8yJ5P3NOKc22xfj3\n7poP3/823VohBJ9++jl/9E/+KdOU1RfD0DMMPX3XEp3H9T2Tm1BKYKTEWEuhoBGSEANd3zEullRF\nwyQUd71nVBWL5Skna0PRtzknxHtsWSNtpHOOzk8cuoHjYWDftuzbA13bMvrI6WrJxXbNq92eYehJ\nMWS1UvQZ+Sgy/1aEe6txRGg955Xke8pZU/L5+Zbvdkeu+gktZuP+LHJ7dLLkJ5884WKxQMvZcxoj\nIQXcFPI9R+YoP4kk+kDwEzJ4KimptMYIUCGAd7gU6GLk7eHAz16/5fvrO7rJ44JnCJ722M6yOk9h\nFCJl9VVykjQJvGtxwWOLgrOzE85OtlRFkQsz5MUmpZQ9n2WDKuu8UKSILDS+qtm3t7jhhrOFYaUU\nd/3ENMEwRJINTEmSpCUKg5+LVArF/dgxxcT9GvBgt5uDrUKMRCFJUiKNwSjJECcmFxingPfgpsQ0\nRXzIyJTRC9pDy933L9DlispukbImFZogFYn4/t52f4+7x5nMD7dIER0i1jkeFYrPtite3e6ZQuTp\n2Ybfef4xZ6drIOTV1xi8trRJE2NBokRGhXC5uyujQJkihx9LRRKSCcGgKpz1jNbT6h5vGxaqpqoX\nsyXvfd1kS9yvPtI+1NaD8PC3KM4/+Ed/xH/+X/yXaKMZh5G+75iGgbHveP3iO/7n//F/4Muf/012\nn/SOqesxVlEvGvrJ0/Yt7+5uaacRbSve7Hu+P/Y8vnjE+WbL6bJhu1zS7+4I/cD13R1/+c3PefH6\new5tl8/6KZBiILmRaehxIbFdrbjqeo7tkaurS6JKjOOY739CzHwdSWGLWQJG3hVnv+ez9YL/5Mef\n8eev3/IvX76lC/kN895RWcXHTx7x8eNzmrKYf7/ENDmOQ8th7HHAFAX9FDjuDri+QxDQRE7qisfb\nNefLFcuyRJC4PR54d9zz7nCg7440CiqrsgwMSbtd0lR3HIY9MYYcSy/z3TMhGceJ9vaWy+tr+qHH\nGoXRJ4hfeghC8AztEZHyriClpB8HLq8PbHTHs7OCU2MZ90favSHFxDTrlhMiF7cyBGEYomQ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zfQp5GCETUdSW0WS/e9oxs8Rmhs3VAtl4jJoYaWfnfkMLRU/QE9DRAjPkyMydMGz5ASI4nDODHG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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JB+fKWSjjtePzHwLvAxOBMy3zcEHZuy+77/YBLKyjfvMIdzSB7frK138nzKlc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+Hk5MQbKIUNIWwJsfO8nq2ppVJqIPfiFCxeLu9pk/g+d8UrZT8SHKKfZTtS1xE0eru8NCFULCtD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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_ALyF0qQWWd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "bc8973a3-18d3-45d3-88aa-39b851b7adb5"
      },
      "source": [
        "with torch.no_grad():\n",
        "    epoch = 1\n",
        "    sample = torch.randn(16, 150).to(device)\n",
        "    sample = model.decode(sample).cpu()\n",
        "    print(sample.shape)\n",
        "    sample = sample.view(16, 3, 64, 64)\n",
        "    \n",
        "    save_image(sample,\n",
        "                'sample_' + str(epoch) + '.png')\n",
        "    plt.imshow(mpimg.imread('sample_' + str(epoch) + '.png'))\n",
        "    \n",
        "    show(sample, renorm = True, nrow=8)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([16, 12288])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wkWG8JThnKfIGSMbenEl3odI17ZyeRDBmx9qiUkdZ6IQwV4dOw9BJNcaiKOBEwzDMBOsb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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3a3r8IHFYrLZQFtLib0vq6sV+gmJnychc5u0F1EYFlgrmAVh7WK2EpmsQ1yC+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\njFPdgrbzWHcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8-snMdV4X5w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "426a91a5-3a39-4105-b960-4392f6012bc2"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def between(x_from, x_to):\n",
        "  t_from, _ , _ = model.encode(x_from.view(-1, nc * ndf * ngf))\n",
        "  t_to , _ , _ = model.encode(x_to.view(-1, nc * ndf * ngf))\n",
        "\n",
        "  middle = torch.lerp(t_from, t_to, .5)\n",
        "\n",
        "  return model.decode(middle)\n",
        "\n",
        "def linear_interpolation(model, x_from, x_to, steps):\n",
        "    n = steps\n",
        "\n",
        "    t_from, _ , _ = model.encode(x_from.view(-1, nc * ndf * ngf))\n",
        "    t_to , _ , _ = model.encode(x_to.view(-1, nc * ndf * ngf))\n",
        "\n",
        "    t_from = t_from.cpu().detach().numpy()\n",
        "    t_to = t_to.cpu().detach().numpy()\n",
        "\n",
        "    diff = t_to - t_from\n",
        "    inter = np.zeros((n, t_from.shape[1]))\n",
        "    for i in range(n):\n",
        "        inter[i] = t_from + np.linspace(0,1,steps)[i] * t_to\n",
        "\n",
        "    inter = torch.tensor(inter).float().to(device)\n",
        "\n",
        "    return model.decode(inter)\n",
        "\n",
        "\n",
        "def plot_linear_interpolations(x_from, x_to, steps=10):\n",
        "    batch = linear_interpolation(model, x_from, x_to, steps)\n",
        "    batch = batch.view(-1, nc, ndf, ngf)\n",
        "    print(batch.shape)\n",
        "    show(batch.cpu(), renorm = True, nrow=4)\n",
        "\n",
        "def show_single_image(img, name):\n",
        "  save_image(img,\n",
        "                'sample_' + name + '.png')\n",
        "  plt.imshow(mpimg.imread('sample_' + name + '.png'))\n",
        "\n",
        "with torch.no_grad():\n",
        "  img1, _ = data_loader.dataset[1]\n",
        "  img2, _  = data_loader.dataset[2]\n",
        "  img1 = img1.to(device)\n",
        "  img2 = img2.to(device)\n",
        "  img1.unsqueeze_(0)\n",
        "  img2.unsqueeze_(0)\n",
        "\n",
        "  show_single_image(torch.cat((img1, img2)), \"img1\")\n",
        "  \n",
        "  plot_linear_interpolations(img1, img2)\n",
        "\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10, 3, 64, 64])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IVPq0SJYiO05SmPXqiUBEe23m67FOdylRF/1m2YQCKeN8j7rc2R1WBCGUyXxa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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xCpQpO9yqEI2ht0KnQmws2kJslaIF0/VcNQPPmshZ+Hqh7F2FYdshwcEkt4wqoRzDaCJM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\nBoohzok8n7A5obJFtEeDUsaJECNFIlny303UBe/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sKU3koGgsZFVKFlI24C1N75CtQRqP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\nb03aAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9evjvTUUGBD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "02b97530-8017-4171-db27-9a7ff25b9714"
      },
      "source": [
        "img3 = between(img1, img2)\n",
        "show_single_image(img3, \"img3\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAAjCAYAAACU030xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNKb8xBfsMoUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwKHOH1It2WT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#ignore utility function. remove it later on only relevent to cnn\n",
        "\n",
        "def outputSize(in_size, kernel_size, stride, padding):\n",
        "  output = int((in_size - kernel_size + 2*(padding)) / stride) + 1\n",
        "  return(output)\n",
        "\n",
        "print(outputSize(32,4,2,0))"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}