HW3/VAE_clustering_sol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "91eWH3ZiMtxH"
   },
   "source": [
    "# VAE for MNIST clustering and generation\n",
    "\n",
    "The goal of this notebook is to explore some recent works dealing with variational auto-encoder (VAE).\n",
    "\n",
    "We will use MNIST dataset and a basic VAE architecture. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "onVZXPp6MtxP"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from torchvision.utils import save_image\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.metrics.cluster import normalized_mutual_info_score\n",
    "\n",
    "def show(img):\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest')\n",
    "    \n",
    "def plot_reconstruction(model, n=24):\n",
    "    x,_ = next(iter(data_loader))\n",
    "    x = x[:n,:,:,:].to(device)\n",
    "    try:\n",
    "        out, _, _, log_p = model(x.view(-1, image_size)) \n",
    "    except:\n",
    "        out, _, _ = model(x.view(-1, image_size)) \n",
    "    x_concat = torch.cat([x.view(-1, 1, 28, 28), out.view(-1, 1, 28, 28)], dim=3)\n",
    "    out_grid = torchvision.utils.make_grid(x_concat).cpu().data\n",
    "    show(out_grid)\n",
    "\n",
    "def plot_generation(model, n=24):\n",
    "    with torch.no_grad():\n",
    "        z = torch.randn(n, z_dim).to(device)\n",
    "        out = model.decode(z).view(-1, 1, 28, 28)\n",
    "\n",
    "    out_grid = torchvision.utils.make_grid(out).cpu()\n",
    "    show(out_grid)\n",
    "\n",
    "def plot_conditional_generation(model, n=8, fix_number=None):\n",
    "    with torch.no_grad():\n",
    "        matrix = np.zeros((n,n_classes))\n",
    "        matrix[:,0] = 1\n",
    "\n",
    "        if fix_number is None:\n",
    "            final = matrix[:]\n",
    "            for i in range(1,n_classes):\n",
    "                final = np.vstack((final,np.roll(matrix,i)))\n",
    "            #z = torch.randn(8*n_classes, z_dim).to(device)\n",
    "            z = torch.randn(8, z_dim)\n",
    "            z = z.repeat(n_classes,1).to(device)\n",
    "            y_onehot = torch.tensor(final).type(torch.FloatTensor).to(device)\n",
    "            out = model.decode(z,y_onehot).view(-1, 1, 28, 28)\n",
    "        else:\n",
    "            z = torch.randn(n, z_dim).to(device)\n",
    "            y_onehot = torch.tensor(np.roll(matrix, fix_number)).type(torch.FloatTensor).to(device)\n",
    "            out = model.decode(z,y_onehot).view(-1, 1, 28, 28)\n",
    "\n",
    "    out_grid = torchvision.utils.make_grid(out).cpu()\n",
    "    show(out_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ic647cGkMtxb"
   },
   "outputs": [],
   "source": [
    "# Device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Create a directory if not exists\n",
    "sample_dir = 'samples'\n",
    "if not os.path.exists(sample_dir):\n",
    "    os.makedirs(sample_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pgwvL5SvMtxm"
   },
   "outputs": [],
   "source": [
    "data_dir = 'data'\n",
    "# MNIST dataset\n",
    "dataset = torchvision.datasets.MNIST(root=data_dir,\n",
    "                                     train=True,\n",
    "                                     transform=transforms.ToTensor(),\n",
    "                                     download=True)\n",
    "\n",
    "# Data loader\n",
    "data_loader = torch.utils.data.DataLoader(dataset=dataset,\n",
    "                                          batch_size=128, \n",
    "                                          shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    torchvision.datasets.MNIST(data_dir, train=False, download=True, transform=transforms.ToTensor()),\n",
    "    batch_size=10, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UESJllvCMtxu"
   },
   "source": [
    "# Variational Autoencoders\n",
    "\n",
    "Consider a latent variable model with a data variable $x\\in \\mathcal{X}$ and a latent variable $z\\in \\mathcal{Z}$, $p(z,x) = p(z)p_\\theta(x|z)$. Given the data $x_1,\\dots, x_n$, we want to train the model by maximizing the marginal log-likelihood:\n",
    "\\begin{eqnarray*}\n",
    "\\mathcal{L} = \\mathbf{E}_{p_d(x)}\\left[\\log p_\\theta(x)\\right]=\\mathbf{E}_{p_d(x)}\\left[\\log \\int_{\\mathcal{Z}}p_{\\theta}(x|z)p(z)dz\\right],\n",
    "  \\end{eqnarray*}\n",
    "  where $p_d$ denotes the empirical distribution of $X$: $p_d(x) =\\frac{1}{n}\\sum_{i=1}^n \\delta_{x_i}(x)$.\n",
    "\n",
    " To avoid the (often) difficult computation of the integral above, the idea behind variational methods is to instead maximize a lower bound to the log-likelihood:\n",
    "  \\begin{eqnarray*}\n",
    "\\mathcal{L} \\geq L(p_\\theta(x|z),q(z|x)) =\\mathbf{E}_{p_d(x)}\\left[\\mathbf{E}_{q(z|x)}\\left[\\log p_\\theta(x|z)\\right]-\\mathrm{KL}\\left( q(z|x)||p(z)\\right)\\right].\n",
    "  \\end{eqnarray*}\n",
    "  Any choice of $q(z|x)$ gives a valid lower bound. Variational autoencoders replace the variational posterior $q(z|x)$ by an inference network $q_{\\phi}(z|x)$ that is trained together with $p_{\\theta}(x|z)$ to jointly maximize $L(p_\\theta,q_\\phi)$.\n",
    "  \n",
    "The variational posterior $q_{\\phi}(z|x)$ is also called the **encoder** and the generative model $p_{\\theta}(x|z)$, the **decoder** or generator.\n",
    "\n",
    "The first term $\\mathbf{E}_{q(z|x)}\\left[\\log p_\\theta(x|z)\\right]$ is the negative reconstruction error. Indeed under a gaussian assumption i.e. $p_{\\theta}(x|z) = \\mathcal{N}(\\mu_{\\theta}(z), I)$ the term $\\log p_\\theta(x|z)$ reduces to $\\propto \\|x-\\mu_\\theta(z)\\|^2$, which is often used in practice. The term $\\mathrm{KL}\\left( q(z|x)||p(z)\\right)$ can be seen as a regularization term, where the variational posterior $q_\\phi(z|x)$ should be matched to the prior $p(z)= \\mathcal{N}(0, I)$.\n",
    "\n",
    "Variational Autoencoders were introduced by [Kingma and Welling (2013)](https://arxiv.org/abs/1312.6114), see also [(Doersch, 2016)](https://arxiv.org/abs/1606.05908) for a tutorial.\n",
    "\n",
    "There are various examples of VAE in PyTorch available [here](https://github.com/pytorch/examples/tree/master/vae) or [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/variational_autoencoder/main.py#L38-L65). The code below is taken from this last source.\n",
    "\n",
    "![A variational autoencoder.](vae.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TWtQVnsAMtxw"
   },
   "outputs": [],
   "source": [
    "# Hyper-parameters\n",
    "image_size = 784\n",
    "h_dim = 400\n",
    "z_dim = 20\n",
    "num_epochs = 15\n",
    "learning_rate = 1e-3\n",
    "\n",
    "# VAE model\n",
    "class VAE(nn.Module):\n",
    "    def __init__(self, image_size=784, h_dim=400, z_dim=20):\n",
    "        super(VAE, self).__init__()\n",
    "        self.fc1 = nn.Linear(image_size, h_dim)\n",
    "        self.fc2 = nn.Linear(h_dim, z_dim)\n",
    "        self.fc3 = nn.Linear(h_dim, z_dim)\n",
    "        self.fc4 = nn.Linear(z_dim, h_dim)\n",
    "        self.fc5 = nn.Linear(h_dim, image_size)\n",
    "        \n",
    "    def encode(self, x):\n",
    "        h = F.relu(self.fc1(x))\n",
    "        return self.fc2(h), self.fc3(h)\n",
    "    \n",
    "    def reparameterize(self, mu, log_var):\n",
    "        std = torch.exp(log_var/2)\n",
    "        eps = torch.randn_like(std)\n",
    "        return mu + eps * std\n",
    "\n",
    "    def decode(self, z):\n",
    "        h = F.relu(self.fc4(z))\n",
    "        return torch.sigmoid(self.fc5(h))\n",
    "    \n",
    "    def forward(self, x):\n",
    "        mu, log_var = self.encode(x)\n",
    "        z = self.reparameterize(mu, log_var)\n",
    "        x_reconst = self.decode(z)\n",
    "        return x_reconst, mu, log_var\n",
    "\n",
    "model = VAE().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2klHMA7TMtx2"
   },
   "source": [
    "Here for the loss, instead of MSE for the reconstruction loss, we take Binary Cross-Entropy. The code below is still from the PyTorch tutorial (with minor modifications to avoid warnings!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_hs3Wnd8Mtx4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/15], Step [10/469], Reconst Loss: 274.4249, KL Div: 30.9702\n",
      "Epoch[1/15], Step [20/469], Reconst Loss: 229.9410, KL Div: 9.0791\n",
      "Epoch[1/15], Step [30/469], Reconst Loss: 216.6197, KL Div: 9.6331\n",
      "Epoch[1/15], Step [40/469], Reconst Loss: 220.2027, KL Div: 5.1560\n",
      "Epoch[1/15], Step [50/469], Reconst Loss: 209.2249, KL Div: 5.2660\n",
      "Epoch[1/15], Step [60/469], Reconst Loss: 199.6494, KL Div: 6.6996\n",
      "Epoch[1/15], Step [70/469], Reconst Loss: 185.4102, KL Div: 9.1160\n",
      "Epoch[1/15], Step [80/469], Reconst Loss: 175.4594, KL Div: 9.4649\n",
      "Epoch[1/15], Step [90/469], Reconst Loss: 178.5814, KL Div: 9.8404\n",
      "Epoch[1/15], Step [100/469], Reconst Loss: 181.4238, KL Div: 9.2372\n",
      "Epoch[1/15], Step [110/469], Reconst Loss: 171.6484, KL Div: 11.2110\n",
      "Epoch[1/15], Step [120/469], Reconst Loss: 163.3552, KL Div: 12.1876\n",
      "Epoch[1/15], Step [130/469], Reconst Loss: 160.4736, KL Div: 12.5166\n",
      "Epoch[1/15], Step [140/469], Reconst Loss: 152.8711, KL Div: 13.5351\n",
      "Epoch[1/15], Step [150/469], Reconst Loss: 151.7530, KL Div: 13.4490\n",
      "Epoch[1/15], Step [160/469], Reconst Loss: 147.5121, KL Div: 14.2080\n",
      "Epoch[1/15], Step [170/469], Reconst Loss: 139.9576, KL Div: 14.6621\n",
      "Epoch[1/15], Step [180/469], Reconst Loss: 140.7102, KL Div: 15.1464\n",
      "Epoch[1/15], Step [190/469], Reconst Loss: 148.8132, KL Div: 15.9431\n",
      "Epoch[1/15], Step [200/469], Reconst Loss: 137.5571, KL Div: 15.8317\n",
      "Epoch[1/15], Step [210/469], Reconst Loss: 133.1943, KL Div: 14.5118\n",
      "Epoch[1/15], Step [220/469], Reconst Loss: 137.5314, KL Div: 16.3048\n",
      "Epoch[1/15], Step [230/469], Reconst Loss: 132.5639, KL Div: 15.8101\n",
      "Epoch[1/15], Step [240/469], Reconst Loss: 129.3043, KL Div: 17.3522\n",
      "Epoch[1/15], Step [250/469], Reconst Loss: 125.3282, KL Div: 15.9233\n",
      "Epoch[1/15], Step [260/469], Reconst Loss: 127.7727, KL Div: 16.5653\n",
      "Epoch[1/15], Step [270/469], Reconst Loss: 137.0229, KL Div: 16.4404\n",
      "Epoch[1/15], Step [280/469], Reconst Loss: 121.5221, KL Div: 17.1854\n",
      "Epoch[1/15], Step [290/469], Reconst Loss: 123.5178, KL Div: 17.6883\n",
      "Epoch[1/15], Step [300/469], Reconst Loss: 126.5418, KL Div: 17.3703\n",
      "Epoch[1/15], Step [310/469], Reconst Loss: 119.3786, KL Div: 18.9491\n",
      "Epoch[1/15], Step [320/469], Reconst Loss: 125.7547, KL Div: 17.0573\n",
      "Epoch[1/15], Step [330/469], Reconst Loss: 119.3385, KL Div: 19.3313\n",
      "Epoch[1/15], Step [340/469], Reconst Loss: 120.1938, KL Div: 18.6243\n",
      "Epoch[1/15], Step [350/469], Reconst Loss: 120.7165, KL Div: 17.6002\n",
      "Epoch[1/15], Step [360/469], Reconst Loss: 120.3159, KL Div: 20.2072\n",
      "Epoch[1/15], Step [370/469], Reconst Loss: 113.1504, KL Div: 19.7524\n",
      "Epoch[1/15], Step [380/469], Reconst Loss: 112.6654, KL Div: 19.6542\n",
      "Epoch[1/15], Step [390/469], Reconst Loss: 112.7724, KL Div: 19.4203\n",
      "Epoch[1/15], Step [400/469], Reconst Loss: 108.1220, KL Div: 20.3896\n",
      "Epoch[1/15], Step [410/469], Reconst Loss: 111.7519, KL Div: 19.6011\n",
      "Epoch[1/15], Step [420/469], Reconst Loss: 113.5010, KL Div: 19.8921\n",
      "Epoch[1/15], Step [430/469], Reconst Loss: 113.1425, KL Div: 21.3789\n",
      "Epoch[1/15], Step [440/469], Reconst Loss: 108.7857, KL Div: 21.3311\n",
      "Epoch[1/15], Step [450/469], Reconst Loss: 110.5154, KL Div: 19.4850\n",
      "Epoch[1/15], Step [460/469], Reconst Loss: 110.0282, KL Div: 20.9758\n",
      "Epoch[2/15], Step [10/469], Reconst Loss: 108.6108, KL Div: 21.9160\n",
      "Epoch[2/15], Step [20/469], Reconst Loss: 110.0743, KL Div: 21.5153\n",
      "Epoch[2/15], Step [30/469], Reconst Loss: 106.0235, KL Div: 20.7913\n",
      "Epoch[2/15], Step [40/469], Reconst Loss: 111.5792, KL Div: 19.9969\n",
      "Epoch[2/15], Step [50/469], Reconst Loss: 105.0126, KL Div: 21.1948\n",
      "Epoch[2/15], Step [60/469], Reconst Loss: 108.1000, KL Div: 20.1197\n",
      "Epoch[2/15], Step [70/469], Reconst Loss: 109.3067, KL Div: 21.9268\n",
      "Epoch[2/15], Step [80/469], Reconst Loss: 101.9170, KL Div: 21.0355\n",
      "Epoch[2/15], Step [90/469], Reconst Loss: 104.6416, KL Div: 21.6515\n",
      "Epoch[2/15], Step [100/469], Reconst Loss: 103.5676, KL Div: 21.3242\n",
      "Epoch[2/15], Step [110/469], Reconst Loss: 103.5885, KL Div: 21.3288\n",
      "Epoch[2/15], Step [120/469], Reconst Loss: 98.9997, KL Div: 21.4276\n",
      "Epoch[2/15], Step [130/469], Reconst Loss: 105.2174, KL Div: 21.9759\n",
      "Epoch[2/15], Step [140/469], Reconst Loss: 101.5262, KL Div: 20.7369\n",
      "Epoch[2/15], Step [150/469], Reconst Loss: 103.6516, KL Div: 22.5991\n",
      "Epoch[2/15], Step [160/469], Reconst Loss: 102.8017, KL Div: 21.9601\n",
      "Epoch[2/15], Step [170/469], Reconst Loss: 96.2024, KL Div: 23.2703\n",
      "Epoch[2/15], Step [180/469], Reconst Loss: 99.2056, KL Div: 21.8057\n",
      "Epoch[2/15], Step [190/469], Reconst Loss: 104.1630, KL Div: 22.6444\n",
      "Epoch[2/15], Step [200/469], Reconst Loss: 99.0035, KL Div: 21.9091\n",
      "Epoch[2/15], Step [210/469], Reconst Loss: 101.5438, KL Div: 21.8681\n",
      "Epoch[2/15], Step [220/469], Reconst Loss: 103.5419, KL Div: 23.7558\n",
      "Epoch[2/15], Step [230/469], Reconst Loss: 101.6714, KL Div: 22.7096\n",
      "Epoch[2/15], Step [240/469], Reconst Loss: 99.0428, KL Div: 21.5331\n",
      "Epoch[2/15], Step [250/469], Reconst Loss: 97.6915, KL Div: 21.8743\n",
      "Epoch[2/15], Step [260/469], Reconst Loss: 99.7202, KL Div: 21.9866\n",
      "Epoch[2/15], Step [270/469], Reconst Loss: 95.7340, KL Div: 22.4468\n",
      "Epoch[2/15], Step [280/469], Reconst Loss: 96.2868, KL Div: 22.9484\n",
      "Epoch[2/15], Step [290/469], Reconst Loss: 94.4559, KL Div: 21.9204\n",
      "Epoch[2/15], Step [300/469], Reconst Loss: 95.1808, KL Div: 22.8113\n",
      "Epoch[2/15], Step [310/469], Reconst Loss: 98.2410, KL Div: 22.9927\n",
      "Epoch[2/15], Step [320/469], Reconst Loss: 97.4819, KL Div: 22.7931\n",
      "Epoch[2/15], Step [330/469], Reconst Loss: 97.9455, KL Div: 22.5397\n",
      "Epoch[2/15], Step [340/469], Reconst Loss: 92.3900, KL Div: 23.3101\n",
      "Epoch[2/15], Step [350/469], Reconst Loss: 99.2814, KL Div: 22.1574\n",
      "Epoch[2/15], Step [360/469], Reconst Loss: 96.1796, KL Div: 24.1932\n",
      "Epoch[2/15], Step [370/469], Reconst Loss: 95.1391, KL Div: 22.7190\n",
      "Epoch[2/15], Step [380/469], Reconst Loss: 94.7956, KL Div: 24.0685\n",
      "Epoch[2/15], Step [390/469], Reconst Loss: 100.5378, KL Div: 23.3642\n",
      "Epoch[2/15], Step [400/469], Reconst Loss: 96.2600, KL Div: 24.3748\n",
      "Epoch[2/15], Step [410/469], Reconst Loss: 93.8126, KL Div: 22.7703\n",
      "Epoch[2/15], Step [420/469], Reconst Loss: 94.0425, KL Div: 22.0155\n",
      "Epoch[2/15], Step [430/469], Reconst Loss: 93.8920, KL Div: 22.9503\n",
      "Epoch[2/15], Step [440/469], Reconst Loss: 98.8031, KL Div: 23.5319\n",
      "Epoch[2/15], Step [450/469], Reconst Loss: 94.3073, KL Div: 24.2399\n",
      "Epoch[2/15], Step [460/469], Reconst Loss: 95.1859, KL Div: 23.4530\n",
      "Epoch[3/15], Step [10/469], Reconst Loss: 96.0833, KL Div: 23.7595\n",
      "Epoch[3/15], Step [20/469], Reconst Loss: 92.7205, KL Div: 23.8061\n",
      "Epoch[3/15], Step [30/469], Reconst Loss: 90.4017, KL Div: 23.9740\n",
      "Epoch[3/15], Step [40/469], Reconst Loss: 94.7779, KL Div: 24.0227\n",
      "Epoch[3/15], Step [50/469], Reconst Loss: 96.2499, KL Div: 24.3682\n",
      "Epoch[3/15], Step [60/469], Reconst Loss: 95.3323, KL Div: 22.8691\n",
      "Epoch[3/15], Step [70/469], Reconst Loss: 91.0731, KL Div: 24.0956\n",
      "Epoch[3/15], Step [80/469], Reconst Loss: 91.6078, KL Div: 23.6420\n",
      "Epoch[3/15], Step [90/469], Reconst Loss: 93.4756, KL Div: 23.6099\n",
      "Epoch[3/15], Step [100/469], Reconst Loss: 98.2104, KL Div: 23.8500\n",
      "Epoch[3/15], Step [110/469], Reconst Loss: 91.7979, KL Div: 24.2750\n",
      "Epoch[3/15], Step [120/469], Reconst Loss: 91.9906, KL Div: 23.9244\n",
      "Epoch[3/15], Step [130/469], Reconst Loss: 90.9475, KL Div: 23.8035\n",
      "Epoch[3/15], Step [140/469], Reconst Loss: 89.6533, KL Div: 24.1966\n",
      "Epoch[3/15], Step [150/469], Reconst Loss: 91.7733, KL Div: 24.4114\n",
      "Epoch[3/15], Step [160/469], Reconst Loss: 92.4017, KL Div: 23.5743\n",
      "Epoch[3/15], Step [170/469], Reconst Loss: 89.6613, KL Div: 23.2682\n",
      "Epoch[3/15], Step [180/469], Reconst Loss: 95.6643, KL Div: 24.6821\n",
      "Epoch[3/15], Step [190/469], Reconst Loss: 88.9369, KL Div: 23.4090\n",
      "Epoch[3/15], Step [200/469], Reconst Loss: 92.3000, KL Div: 24.6080\n",
      "Epoch[3/15], Step [210/469], Reconst Loss: 85.5336, KL Div: 24.2951\n",
      "Epoch[3/15], Step [220/469], Reconst Loss: 88.8066, KL Div: 23.6887\n",
      "Epoch[3/15], Step [230/469], Reconst Loss: 87.2263, KL Div: 24.4407\n",
      "Epoch[3/15], Step [240/469], Reconst Loss: 90.3817, KL Div: 22.7187\n",
      "Epoch[3/15], Step [250/469], Reconst Loss: 88.7028, KL Div: 25.0545\n",
      "Epoch[3/15], Step [260/469], Reconst Loss: 92.8904, KL Div: 24.5528\n",
      "Epoch[3/15], Step [270/469], Reconst Loss: 89.4183, KL Div: 24.1257\n",
      "Epoch[3/15], Step [280/469], Reconst Loss: 92.3767, KL Div: 24.0200\n",
      "Epoch[3/15], Step [290/469], Reconst Loss: 91.4148, KL Div: 24.4959\n",
      "Epoch[3/15], Step [300/469], Reconst Loss: 85.1498, KL Div: 23.6849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[3/15], Step [310/469], Reconst Loss: 90.5817, KL Div: 24.2304\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[6/15], Step [150/469], Reconst Loss: 82.9623, KL Div: 25.0615\n",
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      "Epoch[7/15], Step [60/469], Reconst Loss: 81.4048, KL Div: 25.1616\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[8/15], Step [450/469], Reconst Loss: 81.0748, KL Div: 24.9151\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[11/15], Step [270/469], Reconst Loss: 84.4120, KL Div: 25.8943\n",
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      "Epoch[12/15], Step [10/469], Reconst Loss: 78.5574, KL Div: 25.7743\n",
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      "Epoch[12/15], Step [150/469], Reconst Loss: 78.5414, KL Div: 24.7875\n",
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      "Epoch[12/15], Step [200/469], Reconst Loss: 79.9679, KL Div: 25.1619\n",
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      "Epoch[12/15], Step [220/469], Reconst Loss: 77.5126, KL Div: 25.5952\n",
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      "Epoch[13/15], Step [170/469], Reconst Loss: 81.0168, KL Div: 25.1797\n",
      "Epoch[13/15], Step [180/469], Reconst Loss: 79.5359, KL Div: 24.9162\n",
      "Epoch[13/15], Step [190/469], Reconst Loss: 80.7367, KL Div: 25.7976\n",
      "Epoch[13/15], Step [200/469], Reconst Loss: 78.7575, KL Div: 25.1748\n",
      "Epoch[13/15], Step [210/469], Reconst Loss: 81.8069, KL Div: 25.5718\n",
      "Epoch[13/15], Step [220/469], Reconst Loss: 79.3700, KL Div: 25.5427\n",
      "Epoch[13/15], Step [230/469], Reconst Loss: 79.9502, KL Div: 24.8585\n",
      "Epoch[13/15], Step [240/469], Reconst Loss: 82.0993, KL Div: 26.1432\n",
      "Epoch[13/15], Step [250/469], Reconst Loss: 78.1693, KL Div: 25.3272\n",
      "Epoch[13/15], Step [260/469], Reconst Loss: 81.2849, KL Div: 25.8933\n",
      "Epoch[13/15], Step [270/469], Reconst Loss: 83.3931, KL Div: 25.9795\n",
      "Epoch[13/15], Step [280/469], Reconst Loss: 82.6005, KL Div: 25.9979\n",
      "Epoch[13/15], Step [290/469], Reconst Loss: 77.3617, KL Div: 24.2433\n",
      "Epoch[13/15], Step [300/469], Reconst Loss: 80.7863, KL Div: 25.9108\n",
      "Epoch[13/15], Step [310/469], Reconst Loss: 82.7133, KL Div: 25.8140\n",
      "Epoch[13/15], Step [320/469], Reconst Loss: 83.2826, KL Div: 25.2000\n",
      "Epoch[13/15], Step [330/469], Reconst Loss: 79.1906, KL Div: 25.7351\n",
      "Epoch[13/15], Step [340/469], Reconst Loss: 81.2622, KL Div: 25.6228\n",
      "Epoch[13/15], Step [350/469], Reconst Loss: 80.7307, KL Div: 25.5920\n",
      "Epoch[13/15], Step [360/469], Reconst Loss: 80.0183, KL Div: 25.3629\n",
      "Epoch[13/15], Step [370/469], Reconst Loss: 79.9920, KL Div: 24.7375\n",
      "Epoch[13/15], Step [380/469], Reconst Loss: 79.8062, KL Div: 25.7826\n",
      "Epoch[13/15], Step [390/469], Reconst Loss: 80.7388, KL Div: 24.9332\n",
      "Epoch[13/15], Step [400/469], Reconst Loss: 76.6224, KL Div: 25.5548\n",
      "Epoch[13/15], Step [410/469], Reconst Loss: 77.3383, KL Div: 25.0891\n",
      "Epoch[13/15], Step [420/469], Reconst Loss: 81.3314, KL Div: 25.8143\n",
      "Epoch[13/15], Step [430/469], Reconst Loss: 80.5005, KL Div: 25.0617\n",
      "Epoch[13/15], Step [440/469], Reconst Loss: 75.2283, KL Div: 24.5701\n",
      "Epoch[13/15], Step [450/469], Reconst Loss: 80.2755, KL Div: 25.6090\n",
      "Epoch[13/15], Step [460/469], Reconst Loss: 76.6971, KL Div: 24.4310\n",
      "Epoch[14/15], Step [10/469], Reconst Loss: 79.2731, KL Div: 25.1075\n",
      "Epoch[14/15], Step [20/469], Reconst Loss: 79.2047, KL Div: 25.8673\n",
      "Epoch[14/15], Step [30/469], Reconst Loss: 81.4202, KL Div: 25.0154\n",
      "Epoch[14/15], Step [40/469], Reconst Loss: 77.9572, KL Div: 25.2750\n",
      "Epoch[14/15], Step [50/469], Reconst Loss: 80.1524, KL Div: 25.9278\n",
      "Epoch[14/15], Step [60/469], Reconst Loss: 77.9507, KL Div: 25.4062\n",
      "Epoch[14/15], Step [70/469], Reconst Loss: 79.0915, KL Div: 25.4410\n",
      "Epoch[14/15], Step [80/469], Reconst Loss: 79.7717, KL Div: 25.3417\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[14/15], Step [90/469], Reconst Loss: 77.9022, KL Div: 25.1710\n",
      "Epoch[14/15], Step [100/469], Reconst Loss: 79.6319, KL Div: 25.7020\n",
      "Epoch[14/15], Step [110/469], Reconst Loss: 79.2905, KL Div: 24.9275\n",
      "Epoch[14/15], Step [120/469], Reconst Loss: 78.4111, KL Div: 25.2015\n",
      "Epoch[14/15], Step [130/469], Reconst Loss: 75.8103, KL Div: 25.1406\n",
      "Epoch[14/15], Step [140/469], Reconst Loss: 79.2165, KL Div: 25.3476\n",
      "Epoch[14/15], Step [150/469], Reconst Loss: 78.0722, KL Div: 25.2466\n",
      "Epoch[14/15], Step [160/469], Reconst Loss: 81.7863, KL Div: 25.3990\n",
      "Epoch[14/15], Step [170/469], Reconst Loss: 77.8482, KL Div: 25.2353\n",
      "Epoch[14/15], Step [180/469], Reconst Loss: 81.0164, KL Div: 25.7553\n",
      "Epoch[14/15], Step [190/469], Reconst Loss: 81.1258, KL Div: 25.7920\n",
      "Epoch[14/15], Step [200/469], Reconst Loss: 75.8863, KL Div: 24.7707\n",
      "Epoch[14/15], Step [210/469], Reconst Loss: 75.3739, KL Div: 25.9363\n",
      "Epoch[14/15], Step [220/469], Reconst Loss: 82.7865, KL Div: 26.4198\n",
      "Epoch[14/15], Step [230/469], Reconst Loss: 76.4462, KL Div: 24.6032\n",
      "Epoch[14/15], Step [240/469], Reconst Loss: 82.1891, KL Div: 25.7556\n",
      "Epoch[14/15], Step [250/469], Reconst Loss: 80.2442, KL Div: 25.4755\n",
      "Epoch[14/15], Step [260/469], Reconst Loss: 84.0810, KL Div: 26.0179\n",
      "Epoch[14/15], Step [270/469], Reconst Loss: 77.9788, KL Div: 26.3359\n",
      "Epoch[14/15], Step [280/469], Reconst Loss: 82.4291, KL Div: 25.9953\n",
      "Epoch[14/15], Step [290/469], Reconst Loss: 83.1060, KL Div: 26.2420\n",
      "Epoch[14/15], Step [300/469], Reconst Loss: 81.8004, KL Div: 25.3457\n",
      "Epoch[14/15], Step [310/469], Reconst Loss: 77.1296, KL Div: 24.7544\n",
      "Epoch[14/15], Step [320/469], Reconst Loss: 84.0004, KL Div: 26.5196\n",
      "Epoch[14/15], Step [330/469], Reconst Loss: 76.9064, KL Div: 24.7314\n",
      "Epoch[14/15], Step [340/469], Reconst Loss: 81.8702, KL Div: 25.5703\n",
      "Epoch[14/15], Step [350/469], Reconst Loss: 79.1792, KL Div: 25.3160\n",
      "Epoch[14/15], Step [360/469], Reconst Loss: 82.6022, KL Div: 26.2264\n",
      "Epoch[14/15], Step [370/469], Reconst Loss: 77.2587, KL Div: 24.8173\n",
      "Epoch[14/15], Step [380/469], Reconst Loss: 78.3522, KL Div: 24.9748\n",
      "Epoch[14/15], Step [390/469], Reconst Loss: 79.1153, KL Div: 26.1631\n",
      "Epoch[14/15], Step [400/469], Reconst Loss: 80.4595, KL Div: 25.4638\n",
      "Epoch[14/15], Step [410/469], Reconst Loss: 78.3077, KL Div: 25.2384\n",
      "Epoch[14/15], Step [420/469], Reconst Loss: 79.3327, KL Div: 25.3045\n",
      "Epoch[14/15], Step [430/469], Reconst Loss: 80.4745, KL Div: 25.8262\n",
      "Epoch[14/15], Step [440/469], Reconst Loss: 82.0978, KL Div: 25.6600\n",
      "Epoch[14/15], Step [450/469], Reconst Loss: 77.4326, KL Div: 25.0980\n",
      "Epoch[14/15], Step [460/469], Reconst Loss: 84.3124, KL Div: 25.6798\n",
      "Epoch[15/15], Step [10/469], Reconst Loss: 80.1192, KL Div: 25.1292\n",
      "Epoch[15/15], Step [20/469], Reconst Loss: 78.1182, KL Div: 25.0622\n",
      "Epoch[15/15], Step [30/469], Reconst Loss: 74.8649, KL Div: 25.0021\n",
      "Epoch[15/15], Step [40/469], Reconst Loss: 81.4691, KL Div: 25.3147\n",
      "Epoch[15/15], Step [50/469], Reconst Loss: 81.9221, KL Div: 25.5312\n",
      "Epoch[15/15], Step [60/469], Reconst Loss: 79.6230, KL Div: 25.7715\n",
      "Epoch[15/15], Step [70/469], Reconst Loss: 81.1241, KL Div: 26.1866\n",
      "Epoch[15/15], Step [80/469], Reconst Loss: 73.4001, KL Div: 25.1895\n",
      "Epoch[15/15], Step [90/469], Reconst Loss: 80.3092, KL Div: 25.7542\n",
      "Epoch[15/15], Step [100/469], Reconst Loss: 82.0530, KL Div: 25.6882\n",
      "Epoch[15/15], Step [110/469], Reconst Loss: 79.0645, KL Div: 25.5957\n",
      "Epoch[15/15], Step [120/469], Reconst Loss: 78.3089, KL Div: 25.8112\n",
      "Epoch[15/15], Step [130/469], Reconst Loss: 79.9754, KL Div: 25.9689\n",
      "Epoch[15/15], Step [140/469], Reconst Loss: 78.0037, KL Div: 24.8712\n",
      "Epoch[15/15], Step [150/469], Reconst Loss: 82.0263, KL Div: 25.8716\n",
      "Epoch[15/15], Step [160/469], Reconst Loss: 81.4975, KL Div: 25.0676\n",
      "Epoch[15/15], Step [170/469], Reconst Loss: 80.2127, KL Div: 26.2824\n",
      "Epoch[15/15], Step [180/469], Reconst Loss: 78.0029, KL Div: 25.3592\n",
      "Epoch[15/15], Step [190/469], Reconst Loss: 82.1320, KL Div: 26.1620\n",
      "Epoch[15/15], Step [200/469], Reconst Loss: 79.5500, KL Div: 25.3309\n",
      "Epoch[15/15], Step [210/469], Reconst Loss: 78.4155, KL Div: 25.0009\n",
      "Epoch[15/15], Step [220/469], Reconst Loss: 77.4840, KL Div: 25.5200\n",
      "Epoch[15/15], Step [230/469], Reconst Loss: 78.4920, KL Div: 25.2231\n",
      "Epoch[15/15], Step [240/469], Reconst Loss: 80.4169, KL Div: 25.6376\n",
      "Epoch[15/15], Step [250/469], Reconst Loss: 81.0246, KL Div: 25.5845\n",
      "Epoch[15/15], Step [260/469], Reconst Loss: 78.8368, KL Div: 25.1902\n",
      "Epoch[15/15], Step [270/469], Reconst Loss: 78.7375, KL Div: 25.7937\n",
      "Epoch[15/15], Step [280/469], Reconst Loss: 76.2253, KL Div: 24.9034\n",
      "Epoch[15/15], Step [290/469], Reconst Loss: 78.0373, KL Div: 24.4137\n",
      "Epoch[15/15], Step [300/469], Reconst Loss: 80.7347, KL Div: 25.5326\n",
      "Epoch[15/15], Step [310/469], Reconst Loss: 81.7358, KL Div: 26.0397\n",
      "Epoch[15/15], Step [320/469], Reconst Loss: 79.6274, KL Div: 26.7091\n",
      "Epoch[15/15], Step [330/469], Reconst Loss: 80.1726, KL Div: 25.4180\n",
      "Epoch[15/15], Step [340/469], Reconst Loss: 80.6325, KL Div: 25.6766\n",
      "Epoch[15/15], Step [350/469], Reconst Loss: 79.4085, KL Div: 25.9481\n",
      "Epoch[15/15], Step [360/469], Reconst Loss: 80.1698, KL Div: 25.9431\n",
      "Epoch[15/15], Step [370/469], Reconst Loss: 78.7093, KL Div: 24.9542\n",
      "Epoch[15/15], Step [380/469], Reconst Loss: 76.0502, KL Div: 25.3711\n",
      "Epoch[15/15], Step [390/469], Reconst Loss: 77.8484, KL Div: 24.6907\n",
      "Epoch[15/15], Step [400/469], Reconst Loss: 81.1926, KL Div: 25.6157\n",
      "Epoch[15/15], Step [410/469], Reconst Loss: 79.1315, KL Div: 26.1394\n",
      "Epoch[15/15], Step [420/469], Reconst Loss: 77.9824, KL Div: 26.0326\n",
      "Epoch[15/15], Step [430/469], Reconst Loss: 80.6389, KL Div: 25.0332\n",
      "Epoch[15/15], Step [440/469], Reconst Loss: 82.7139, KL Div: 25.6953\n",
      "Epoch[15/15], Step [450/469], Reconst Loss: 78.8075, KL Div: 26.1947\n",
      "Epoch[15/15], Step [460/469], Reconst Loss: 77.1312, KL Div: 24.5642\n"
     ]
    }
   ],
   "source": [
    "# Start training\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (x, _) in enumerate(data_loader):\n",
    "        # Forward pass\n",
    "        x = x.to(device).view(-1, image_size)\n",
    "        x_reconst, mu, log_var = model(x)\n",
    "        \n",
    "        # Compute reconstruction loss and kl divergence\n",
    "        # For KL divergence between Gaussians, see Appendix B in VAE paper or (Doersch, 2016):\n",
    "        # https://arxiv.org/abs/1606.05908\n",
    "        reconst_loss = F.binary_cross_entropy(x_reconst, x, reduction='sum')\n",
    "        kl_div = - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())\n",
    "        \n",
    "        # Backprop and optimize\n",
    "        loss = reconst_loss + kl_div\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if (i+1) % 10 == 0:\n",
    "            print (\"Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}\" \n",
    "                   .format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item()/len(x), kl_div.item()/len(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XinKgE7AMtx-"
   },
   "source": [
    "Let see how our network reconstructs our last batch. We display pairs of original digits and reconstructed version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YLTFvi0SMtyA"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reconstruction(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZC7nauVyMtyF"
   },
   "source": [
    "Let's see now how our network generates new samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ehuLHz4NMtyH"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_generation(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hddb3q43MtyN"
   },
   "source": [
    "Not great, but we did not train our network for long... That being said, we have no control of the generated digits. In the rest of this notebook, we explore ways to generates zeroes, ones, twos and so on. \n",
    "\n",
    "\n",
    "As a by-product, we show how our VAE will allow us to do clustering tahnsk to the Gumbel VAE described below. But before that, we start by cheatin a little bit..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cheating with the 'conditional' VAE\n",
    "\n",
    "We will first use the labels here (like what we did in the course with [Conditional GAN](https://dataflowr.github.io/website/modules/10-generative-adversarial-networks/)). The idea is to modify slightly the architecture above by feeding a onehot version of the label to the decoder in addition to the code computed by the decoder. \n",
    "\n",
    "First code a function transforming a label in its onehot encoding. This function will be used in the training loop (not in the architecture of the neural network!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 10\n",
    "def l_2_onehot(labels,nb_digits=n_classes):\n",
    "    # take labels (from the dataloader) and return labels onehot-encoded\n",
    "    l_onehot = torch.FloatTensor(labels.shape[0], nb_digits)\n",
    "    l_onehot.zero_()\n",
    "    l_onehot.scatter_(1, labels.unsqueeze(1), 1)\n",
    "    return l_onehot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can test it on a batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x,labels) = next(iter(data_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2, 0, 9, 3, 1, 0, 8, 5, 6, 0, 4, 2, 4, 1, 6, 8, 5, 7, 3, 6, 5, 2, 1, 8,\n",
       "        6, 7, 4, 4, 7, 8, 1, 4, 0, 8, 9, 8, 2, 7, 1, 7, 3, 8, 2, 1, 2, 7, 3, 7,\n",
       "        9, 8, 9, 9, 5, 6, 9, 3, 2, 9, 0, 7, 8, 1, 9, 7, 4, 9, 2, 8, 0, 7, 7, 3,\n",
       "        1, 0, 2, 9, 6, 5, 1, 0, 5, 7, 9, 4, 3, 5, 5, 9, 4, 8, 8, 1, 5, 9, 2, 7,\n",
       "        3, 6, 9, 7, 2, 5, 0, 3, 3, 1, 1, 3, 2, 0, 8, 6, 3, 1, 5, 8, 0, 4, 5, 7,\n",
       "        3, 7, 9, 9, 0, 8, 1, 7])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 1.,  ..., 0., 0., 0.],\n",
       "        [1., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 1.],\n",
       "        ...,\n",
       "        [0., 0., 0.,  ..., 0., 1., 0.],\n",
       "        [0., 1., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 1., 0., 0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_2_onehot(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now modify the architecture of the VAE where the decoder takes as input the random code concatenated with the onehot encoding of the label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 10\n",
    "\n",
    "class VAE_Cond(nn.Module):\n",
    "    def __init__(self, image_size=784, h_dim=400, z_dim=20, n_classes = 10):\n",
    "        super(VAE_Cond, self).__init__()\n",
    "        self.fc1 = nn.Linear(image_size, h_dim)\n",
    "        self.fc2 = nn.Linear(h_dim, z_dim)\n",
    "        self.fc3 = nn.Linear(h_dim, z_dim)\n",
    "        self.fc4 = nn.Linear(z_dim+n_classes, h_dim)\n",
    "        self.fc5 = nn.Linear(h_dim, image_size)\n",
    "        \n",
    "    def encode(self, x):\n",
    "        h = F.relu(self.fc1(x))\n",
    "        return self.fc2(h), self.fc3(h)\n",
    "    \n",
    "    def reparameterize(self, mu, log_var):\n",
    "        std = torch.exp(log_var/2)\n",
    "        eps = torch.randn_like(std)\n",
    "        return mu + eps * std\n",
    "\n",
    "    def decode(self, z, l_onehot):\n",
    "        x = torch.cat([z, l_onehot], 1)\n",
    "        h = F.relu(self.fc4(x))\n",
    "        return torch.sigmoid(self.fc5(h))     \n",
    "    \n",
    "    def forward(self, x, l_onehot):\n",
    "        mu, log_var = self.encode(x)\n",
    "        z = self.reparameterize(mu, log_var)\n",
    "        x_reconst = self.decode(z,l_onehot)\n",
    "        return x_reconst, mu, log_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test your new model on a batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0.5013, 0.5295, 0.4564,  ..., 0.4432, 0.4528, 0.5620],\n",
       "         [0.4989, 0.5340, 0.5662,  ..., 0.4467, 0.4913, 0.5849],\n",
       "         [0.4751, 0.5649, 0.5416,  ..., 0.4234, 0.4822, 0.5361],\n",
       "         ...,\n",
       "         [0.4473, 0.4697, 0.5232,  ..., 0.5286, 0.3867, 0.5601],\n",
       "         [0.4885, 0.5402, 0.5513,  ..., 0.4202, 0.4395, 0.5738],\n",
       "         [0.4421, 0.4926, 0.4449,  ..., 0.4485, 0.4463, 0.5546]],\n",
       "        device='cuda:0', grad_fn=<SigmoidBackward0>),\n",
       " tensor([[-0.0433, -0.0832,  0.0091,  ...,  0.1585, -0.0184,  0.0606],\n",
       "         [ 0.1620, -0.0522, -0.0598,  ...,  0.1157, -0.1724, -0.1407],\n",
       "         [-0.0765, -0.0521,  0.0484,  ...,  0.0335, -0.0362, -0.0366],\n",
       "         ...,\n",
       "         [ 0.0767, -0.1065,  0.0955,  ...,  0.1742, -0.0872,  0.1011],\n",
       "         [-0.0377, -0.0847, -0.0209,  ...,  0.0493, -0.0168,  0.1104],\n",
       "         [-0.0784, -0.1386,  0.1181,  ...,  0.1484, -0.0814, -0.0636]],\n",
       "        device='cuda:0', grad_fn=<AddmmBackward0>),\n",
       " tensor([[ 7.5633e-03, -1.4208e-01,  8.1562e-03,  ...,  6.4414e-02,\n",
       "          -1.6465e-01,  5.3849e-02],\n",
       "         [ 5.2431e-02,  9.6377e-02,  7.9829e-02,  ..., -3.1797e-03,\n",
       "           1.8520e-02,  1.7663e-01],\n",
       "         [ 6.8508e-02,  8.8280e-02,  6.9992e-02,  ...,  5.8736e-02,\n",
       "           2.4661e-05,  1.0259e-01],\n",
       "         ...,\n",
       "         [ 6.3729e-02,  8.0536e-02,  6.0001e-02,  ...,  1.3831e-01,\n",
       "           7.9770e-02,  1.8363e-01],\n",
       "         [ 1.0092e-02,  8.0233e-02,  9.7590e-02,  ...,  4.9509e-02,\n",
       "          -2.3440e-02,  1.3059e-01],\n",
       "         [-3.6676e-02,  1.5502e-01,  3.7187e-02,  ...,  1.4365e-01,\n",
       "           3.6746e-03,  2.3963e-01]], device='cuda:0', grad_fn=<AddmmBackward0>))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_C = VAE_Cond().to(device)\n",
    "x = x.to(device).view(-1, image_size)\n",
    "l_onehot = l_2_onehot(labels)\n",
    "l_onehot = l_onehot.to(device)\n",
    "model_C(x, l_onehot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can modify the training loop of your network. The parameter $\\beta$ will allow you to scale the KL term in your loss as explained in the [$\\beta$-VAE paper](https://openreview.net/forum?id=Sy2fzU9gl) see formula (4) in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_C(model, data_loader=data_loader,num_epochs=num_epochs, beta=10., verbose=True):\n",
    "    nmi_scores = []\n",
    "    model.train(True)\n",
    "    for epoch in range(num_epochs):\n",
    "        for i, (x, labels) in enumerate(data_loader):\n",
    "            # Forward pass\n",
    "            x = x.to(device).view(-1, image_size)\n",
    "            l_onehot = l_2_onehot(labels)\n",
    "            l_onehot = l_onehot.to(device)\n",
    "            labels = labels.to(device)\n",
    "            x_reconst, mu, log_var = model(x,l_onehot)\n",
    "            \n",
    "            \n",
    "            reconst_loss = F.binary_cross_entropy(x_reconst, x, reduction='sum')\n",
    "            kl_div =  - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())\n",
    "            \n",
    "            # Backprop and optimize\n",
    "            loss = reconst_loss + beta*kl_div\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            if verbose:\n",
    "                if (i+1) % 10 == 0:\n",
    "                    print(\"Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}\"\n",
    "                           .format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item()/len(x),\n",
    "                                   kl_div.item()/len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_C = VAE_Cond().to(device)\n",
    "optimizer = torch.optim.Adam(model_C.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/15], Step [10/469], Reconst Loss: 348.3138, KL Div: 1.9303\n",
      "Epoch[1/15], Step [20/469], Reconst Loss: 259.1938, KL Div: 1.3214\n",
      "Epoch[1/15], Step [30/469], Reconst Loss: 220.3017, KL Div: 0.8366\n",
      "Epoch[1/15], Step [40/469], Reconst Loss: 220.6763, KL Div: 0.4514\n",
      "Epoch[1/15], Step [50/469], Reconst Loss: 222.4193, KL Div: 0.3216\n",
      "Epoch[1/15], Step [60/469], Reconst Loss: 198.1614, KL Div: 0.3989\n",
      "Epoch[1/15], Step [70/469], Reconst Loss: 201.1050, KL Div: 0.2724\n",
      "Epoch[1/15], Step [80/469], Reconst Loss: 203.7372, KL Div: 0.2940\n",
      "Epoch[1/15], Step [90/469], Reconst Loss: 201.1166, KL Div: 0.2300\n",
      "Epoch[1/15], Step [100/469], Reconst Loss: 202.6196, KL Div: 0.2142\n",
      "Epoch[1/15], Step [110/469], Reconst Loss: 194.7351, KL Div: 0.3439\n",
      "Epoch[1/15], Step [120/469], Reconst Loss: 195.5732, KL Div: 0.3358\n",
      "Epoch[1/15], Step [130/469], Reconst Loss: 189.0608, KL Div: 0.3422\n",
      "Epoch[1/15], Step [140/469], Reconst Loss: 183.9213, KL Div: 0.3644\n",
      "Epoch[1/15], Step [150/469], Reconst Loss: 186.0403, KL Div: 0.4002\n",
      "Epoch[1/15], Step [160/469], Reconst Loss: 189.7333, KL Div: 0.4194\n",
      "Epoch[1/15], Step [170/469], Reconst Loss: 183.5538, KL Div: 0.3797\n",
      "Epoch[1/15], Step [180/469], Reconst Loss: 183.6634, KL Div: 0.4360\n",
      "Epoch[1/15], Step [190/469], Reconst Loss: 184.5751, KL Div: 0.4012\n",
      "Epoch[1/15], Step [200/469], Reconst Loss: 180.8649, KL Div: 0.4204\n",
      "Epoch[1/15], Step [210/469], Reconst Loss: 174.4342, KL Div: 0.3793\n",
      "Epoch[1/15], Step [220/469], Reconst Loss: 181.7150, KL Div: 0.3966\n",
      "Epoch[1/15], Step [230/469], Reconst Loss: 179.0967, KL Div: 0.4494\n",
      "Epoch[1/15], Step [240/469], Reconst Loss: 175.9166, KL Div: 0.4385\n",
      "Epoch[1/15], Step [250/469], Reconst Loss: 175.4487, KL Div: 0.4490\n",
      "Epoch[1/15], Step [260/469], Reconst Loss: 174.7944, KL Div: 0.4769\n",
      "Epoch[1/15], Step [270/469], Reconst Loss: 176.3172, KL Div: 0.4973\n",
      "Epoch[1/15], Step [280/469], Reconst Loss: 174.9580, KL Div: 0.4985\n",
      "Epoch[1/15], Step [290/469], Reconst Loss: 175.0936, KL Div: 0.4671\n",
      "Epoch[1/15], Step [300/469], Reconst Loss: 178.5451, KL Div: 0.5255\n",
      "Epoch[1/15], Step [310/469], Reconst Loss: 169.0513, KL Div: 0.5486\n",
      "Epoch[1/15], Step [320/469], Reconst Loss: 174.3408, KL Div: 0.5957\n",
      "Epoch[1/15], Step [330/469], Reconst Loss: 174.2124, KL Div: 0.4659\n",
      "Epoch[1/15], Step [340/469], Reconst Loss: 165.8758, KL Div: 0.4787\n",
      "Epoch[1/15], Step [350/469], Reconst Loss: 163.2166, KL Div: 0.4721\n",
      "Epoch[1/15], Step [360/469], Reconst Loss: 170.7547, KL Div: 0.4947\n",
      "Epoch[1/15], Step [370/469], Reconst Loss: 169.9909, KL Div: 0.5605\n",
      "Epoch[1/15], Step [380/469], Reconst Loss: 165.0017, KL Div: 0.5376\n",
      "Epoch[1/15], Step [390/469], Reconst Loss: 165.3327, KL Div: 0.5922\n",
      "Epoch[1/15], Step [400/469], Reconst Loss: 164.4914, KL Div: 0.4920\n",
      "Epoch[1/15], Step [410/469], Reconst Loss: 168.1418, KL Div: 0.5504\n",
      "Epoch[1/15], Step [420/469], Reconst Loss: 177.2805, KL Div: 0.5865\n",
      "Epoch[1/15], Step [430/469], Reconst Loss: 171.3557, KL Div: 0.5357\n",
      "Epoch[1/15], Step [440/469], Reconst Loss: 169.6130, KL Div: 0.6187\n",
      "Epoch[1/15], Step [450/469], Reconst Loss: 165.4727, KL Div: 0.6406\n",
      "Epoch[1/15], Step [460/469], Reconst Loss: 163.9699, KL Div: 0.5886\n",
      "Epoch[2/15], Step [10/469], Reconst Loss: 170.1551, KL Div: 0.5903\n",
      "Epoch[2/15], Step [20/469], Reconst Loss: 174.6474, KL Div: 0.7058\n",
      "Epoch[2/15], Step [30/469], Reconst Loss: 171.2459, KL Div: 0.5959\n",
      "Epoch[2/15], Step [40/469], Reconst Loss: 167.2351, KL Div: 0.5697\n",
      "Epoch[2/15], Step [50/469], Reconst Loss: 157.7852, KL Div: 0.5009\n",
      "Epoch[2/15], Step [60/469], Reconst Loss: 166.6225, KL Div: 0.5980\n",
      "Epoch[2/15], Step [70/469], Reconst Loss: 166.3923, KL Div: 0.6105\n",
      "Epoch[2/15], Step [80/469], Reconst Loss: 169.5254, KL Div: 0.6972\n",
      "Epoch[2/15], Step [90/469], Reconst Loss: 161.7659, KL Div: 0.6342\n",
      "Epoch[2/15], Step [100/469], Reconst Loss: 163.2037, KL Div: 0.5949\n",
      "Epoch[2/15], Step [110/469], Reconst Loss: 159.1167, KL Div: 0.5324\n",
      "Epoch[2/15], Step [120/469], Reconst Loss: 169.3997, KL Div: 0.6274\n",
      "Epoch[2/15], Step [130/469], Reconst Loss: 165.9974, KL Div: 0.6855\n",
      "Epoch[2/15], Step [140/469], Reconst Loss: 168.7751, KL Div: 0.7153\n",
      "Epoch[2/15], Step [150/469], Reconst Loss: 166.6003, KL Div: 0.6590\n",
      "Epoch[2/15], Step [160/469], Reconst Loss: 164.2107, KL Div: 0.6713\n",
      "Epoch[2/15], Step [170/469], Reconst Loss: 167.0688, KL Div: 0.6530\n",
      "Epoch[2/15], Step [180/469], Reconst Loss: 170.2066, KL Div: 0.8028\n",
      "Epoch[2/15], Step [190/469], Reconst Loss: 158.0072, KL Div: 0.7441\n",
      "Epoch[2/15], Step [200/469], Reconst Loss: 168.7545, KL Div: 0.6901\n",
      "Epoch[2/15], Step [210/469], Reconst Loss: 158.1255, KL Div: 0.6501\n",
      "Epoch[2/15], Step [220/469], Reconst Loss: 168.4579, KL Div: 0.6707\n",
      "Epoch[2/15], Step [230/469], Reconst Loss: 163.1744, KL Div: 0.8419\n",
      "Epoch[2/15], Step [240/469], Reconst Loss: 161.6042, KL Div: 0.6748\n",
      "Epoch[2/15], Step [250/469], Reconst Loss: 162.6628, KL Div: 0.7168\n",
      "Epoch[2/15], Step [260/469], Reconst Loss: 157.0031, KL Div: 0.6595\n",
      "Epoch[2/15], Step [270/469], Reconst Loss: 159.7324, KL Div: 0.6537\n",
      "Epoch[2/15], Step [280/469], Reconst Loss: 160.9343, KL Div: 0.7165\n",
      "Epoch[2/15], Step [290/469], Reconst Loss: 158.4603, KL Div: 0.7056\n",
      "Epoch[2/15], Step [300/469], Reconst Loss: 156.7262, KL Div: 0.6935\n",
      "Epoch[2/15], Step [310/469], Reconst Loss: 162.3067, KL Div: 0.6489\n",
      "Epoch[2/15], Step [320/469], Reconst Loss: 154.1928, KL Div: 0.6605\n",
      "Epoch[2/15], Step [330/469], Reconst Loss: 156.1786, KL Div: 0.6229\n",
      "Epoch[2/15], Step [340/469], Reconst Loss: 156.1321, KL Div: 0.7499\n",
      "Epoch[2/15], Step [350/469], Reconst Loss: 158.7372, KL Div: 0.6591\n",
      "Epoch[2/15], Step [360/469], Reconst Loss: 163.9691, KL Div: 0.7898\n",
      "Epoch[2/15], Step [370/469], Reconst Loss: 165.7743, KL Div: 0.7801\n",
      "Epoch[2/15], Step [380/469], Reconst Loss: 158.5519, KL Div: 0.7662\n",
      "Epoch[2/15], Step [390/469], Reconst Loss: 174.8660, KL Div: 0.8239\n",
      "Epoch[2/15], Step [400/469], Reconst Loss: 168.9243, KL Div: 0.8699\n",
      "Epoch[2/15], Step [410/469], Reconst Loss: 163.0247, KL Div: 0.8057\n",
      "Epoch[2/15], Step [420/469], Reconst Loss: 163.6171, KL Div: 0.8016\n",
      "Epoch[2/15], Step [430/469], Reconst Loss: 161.1800, KL Div: 0.8832\n",
      "Epoch[2/15], Step [440/469], Reconst Loss: 154.5544, KL Div: 0.7498\n",
      "Epoch[2/15], Step [450/469], Reconst Loss: 156.8999, KL Div: 0.8791\n",
      "Epoch[2/15], Step [460/469], Reconst Loss: 160.7217, KL Div: 0.7972\n",
      "Epoch[3/15], Step [10/469], Reconst Loss: 161.3666, KL Div: 0.7162\n",
      "Epoch[3/15], Step [20/469], Reconst Loss: 161.6166, KL Div: 0.8596\n",
      "Epoch[3/15], Step [30/469], Reconst Loss: 160.8894, KL Div: 0.7546\n",
      "Epoch[3/15], Step [40/469], Reconst Loss: 161.2083, KL Div: 0.9206\n",
      "Epoch[3/15], Step [50/469], Reconst Loss: 163.9599, KL Div: 0.7842\n",
      "Epoch[3/15], Step [60/469], Reconst Loss: 162.4188, KL Div: 0.9422\n",
      "Epoch[3/15], Step [70/469], Reconst Loss: 161.8526, KL Div: 0.9011\n",
      "Epoch[3/15], Step [80/469], Reconst Loss: 164.3313, KL Div: 0.7602\n",
      "Epoch[3/15], Step [90/469], Reconst Loss: 161.9167, KL Div: 0.9524\n",
      "Epoch[3/15], Step [100/469], Reconst Loss: 162.4032, KL Div: 0.9721\n",
      "Epoch[3/15], Step [110/469], Reconst Loss: 159.7360, KL Div: 0.9361\n",
      "Epoch[3/15], Step [120/469], Reconst Loss: 163.5085, KL Div: 0.8463\n",
      "Epoch[3/15], Step [130/469], Reconst Loss: 158.0377, KL Div: 0.8840\n",
      "Epoch[3/15], Step [140/469], Reconst Loss: 162.6781, KL Div: 0.9499\n",
      "Epoch[3/15], Step [150/469], Reconst Loss: 157.3428, KL Div: 0.8003\n",
      "Epoch[3/15], Step [160/469], Reconst Loss: 153.8882, KL Div: 0.9784\n",
      "Epoch[3/15], Step [170/469], Reconst Loss: 164.1519, KL Div: 0.9145\n",
      "Epoch[3/15], Step [180/469], Reconst Loss: 158.2491, KL Div: 1.0404\n",
      "Epoch[3/15], Step [190/469], Reconst Loss: 157.0428, KL Div: 0.9641\n",
      "Epoch[3/15], Step [200/469], Reconst Loss: 153.8805, KL Div: 1.2124\n",
      "Epoch[3/15], Step [210/469], Reconst Loss: 154.8567, KL Div: 0.8728\n",
      "Epoch[3/15], Step [220/469], Reconst Loss: 159.4172, KL Div: 0.9362\n",
      "Epoch[3/15], Step [230/469], Reconst Loss: 160.3731, KL Div: 1.0207\n",
      "Epoch[3/15], Step [240/469], Reconst Loss: 153.6580, KL Div: 1.0133\n",
      "Epoch[3/15], Step [250/469], Reconst Loss: 161.0543, KL Div: 0.9323\n",
      "Epoch[3/15], Step [260/469], Reconst Loss: 149.3970, KL Div: 1.0132\n",
      "Epoch[3/15], Step [270/469], Reconst Loss: 160.0823, KL Div: 0.9459\n",
      "Epoch[3/15], Step [280/469], Reconst Loss: 161.4456, KL Div: 0.9402\n",
      "Epoch[3/15], Step [290/469], Reconst Loss: 156.9413, KL Div: 0.9486\n",
      "Epoch[3/15], Step [300/469], Reconst Loss: 157.1329, KL Div: 1.0987\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[3/15], Step [310/469], Reconst Loss: 155.1509, KL Div: 1.0683\n",
      "Epoch[3/15], Step [320/469], Reconst Loss: 156.0595, KL Div: 1.0628\n",
      "Epoch[3/15], Step [330/469], Reconst Loss: 155.7386, KL Div: 1.0595\n",
      "Epoch[3/15], Step [340/469], Reconst Loss: 157.8554, KL Div: 1.1143\n",
      "Epoch[3/15], Step [350/469], Reconst Loss: 154.3273, KL Div: 1.0680\n",
      "Epoch[3/15], Step [360/469], Reconst Loss: 154.1517, KL Div: 0.9429\n",
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      "Epoch[5/15], Step [390/469], Reconst Loss: 152.1292, KL Div: 1.1667\n",
      "Epoch[5/15], Step [400/469], Reconst Loss: 150.5285, KL Div: 1.2972\n",
      "Epoch[5/15], Step [410/469], Reconst Loss: 149.7953, KL Div: 1.4581\n",
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      "Epoch[5/15], Step [430/469], Reconst Loss: 147.4010, KL Div: 1.4169\n",
      "Epoch[5/15], Step [440/469], Reconst Loss: 139.3034, KL Div: 1.4156\n",
      "Epoch[5/15], Step [450/469], Reconst Loss: 150.3557, KL Div: 1.3077\n",
      "Epoch[5/15], Step [460/469], Reconst Loss: 144.0008, KL Div: 1.4219\n",
      "Epoch[6/15], Step [10/469], Reconst Loss: 147.7289, KL Div: 1.4255\n",
      "Epoch[6/15], Step [20/469], Reconst Loss: 141.7766, KL Div: 1.3507\n",
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      "Epoch[6/15], Step [80/469], Reconst Loss: 152.0627, KL Div: 1.3677\n",
      "Epoch[6/15], Step [90/469], Reconst Loss: 150.2089, KL Div: 1.4072\n",
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      "Epoch[6/15], Step [110/469], Reconst Loss: 154.4452, KL Div: 1.3109\n",
      "Epoch[6/15], Step [120/469], Reconst Loss: 147.5346, KL Div: 1.5086\n",
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      "Epoch[6/15], Step [140/469], Reconst Loss: 153.8084, KL Div: 1.4068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[6/15], Step [150/469], Reconst Loss: 148.6172, KL Div: 1.3533\n",
      "Epoch[6/15], Step [160/469], Reconst Loss: 151.2452, KL Div: 1.4401\n",
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      "Epoch[6/15], Step [320/469], Reconst Loss: 148.3957, KL Div: 1.4066\n",
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      "Epoch[7/15], Step [60/469], Reconst Loss: 145.1938, KL Div: 1.5866\n",
      "Epoch[7/15], Step [70/469], Reconst Loss: 144.4764, KL Div: 1.3080\n",
      "Epoch[7/15], Step [80/469], Reconst Loss: 140.4506, KL Div: 1.4219\n",
      "Epoch[7/15], Step [90/469], Reconst Loss: 154.9051, KL Div: 1.4805\n",
      "Epoch[7/15], Step [100/469], Reconst Loss: 144.3616, KL Div: 1.4640\n",
      "Epoch[7/15], Step [110/469], Reconst Loss: 146.6055, KL Div: 1.3344\n",
      "Epoch[7/15], Step [120/469], Reconst Loss: 144.0857, KL Div: 1.4285\n",
      "Epoch[7/15], Step [130/469], Reconst Loss: 146.6125, KL Div: 1.5696\n",
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      "Epoch[8/15], Step [10/469], Reconst Loss: 147.0304, KL Div: 1.5461\n",
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      "Epoch[8/15], Step [90/469], Reconst Loss: 149.0233, KL Div: 1.5170\n",
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      "Epoch[8/15], Step [440/469], Reconst Loss: 149.0790, KL Div: 1.4607\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[8/15], Step [450/469], Reconst Loss: 149.6499, KL Div: 1.5032\n",
      "Epoch[8/15], Step [460/469], Reconst Loss: 144.6571, KL Div: 1.5003\n",
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      "Epoch[9/15], Step [40/469], Reconst Loss: 152.5769, KL Div: 1.4650\n",
      "Epoch[9/15], Step [50/469], Reconst Loss: 149.8120, KL Div: 1.5962\n",
      "Epoch[9/15], Step [60/469], Reconst Loss: 146.4192, KL Div: 1.6787\n",
      "Epoch[9/15], Step [70/469], Reconst Loss: 143.1331, KL Div: 1.5726\n",
      "Epoch[9/15], Step [80/469], Reconst Loss: 154.0710, KL Div: 1.4054\n",
      "Epoch[9/15], Step [90/469], Reconst Loss: 142.9548, KL Div: 1.7188\n",
      "Epoch[9/15], Step [100/469], Reconst Loss: 153.1425, KL Div: 1.6609\n",
      "Epoch[9/15], Step [110/469], Reconst Loss: 144.5727, KL Div: 1.6193\n",
      "Epoch[9/15], Step [120/469], Reconst Loss: 148.8130, KL Div: 1.5238\n",
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      "Epoch[9/15], Step [160/469], Reconst Loss: 145.1012, KL Div: 1.5358\n",
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      "Epoch[9/15], Step [200/469], Reconst Loss: 143.1655, KL Div: 1.6671\n",
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      "Epoch[9/15], Step [220/469], Reconst Loss: 156.2540, KL Div: 1.7131\n",
      "Epoch[9/15], Step [230/469], Reconst Loss: 142.3564, KL Div: 1.5082\n",
      "Epoch[9/15], Step [240/469], Reconst Loss: 139.5290, KL Div: 1.5556\n",
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      "Epoch[9/15], Step [300/469], Reconst Loss: 142.7892, KL Div: 1.5540\n",
      "Epoch[9/15], Step [310/469], Reconst Loss: 149.5153, KL Div: 1.5914\n",
      "Epoch[9/15], Step [320/469], Reconst Loss: 145.5061, KL Div: 1.6414\n",
      "Epoch[9/15], Step [330/469], Reconst Loss: 146.0486, KL Div: 1.6832\n",
      "Epoch[9/15], Step [340/469], Reconst Loss: 143.6027, KL Div: 1.4647\n",
      "Epoch[9/15], Step [350/469], Reconst Loss: 144.0695, KL Div: 1.6110\n",
      "Epoch[9/15], Step [360/469], Reconst Loss: 140.7512, KL Div: 1.5541\n",
      "Epoch[9/15], Step [370/469], Reconst Loss: 149.1092, KL Div: 1.6298\n",
      "Epoch[9/15], Step [380/469], Reconst Loss: 142.5739, KL Div: 1.5839\n",
      "Epoch[9/15], Step [390/469], Reconst Loss: 149.7933, KL Div: 1.6515\n",
      "Epoch[9/15], Step [400/469], Reconst Loss: 136.2722, KL Div: 1.6167\n",
      "Epoch[9/15], Step [410/469], Reconst Loss: 143.9656, KL Div: 1.5203\n",
      "Epoch[9/15], Step [420/469], Reconst Loss: 145.9910, KL Div: 1.6649\n",
      "Epoch[9/15], Step [430/469], Reconst Loss: 146.7743, KL Div: 1.6007\n",
      "Epoch[9/15], Step [440/469], Reconst Loss: 142.1603, KL Div: 1.6655\n",
      "Epoch[9/15], Step [450/469], Reconst Loss: 145.8399, KL Div: 1.6567\n",
      "Epoch[9/15], Step [460/469], Reconst Loss: 145.3805, KL Div: 1.6287\n",
      "Epoch[10/15], Step [10/469], Reconst Loss: 139.5951, KL Div: 1.5526\n",
      "Epoch[10/15], Step [20/469], Reconst Loss: 143.7885, KL Div: 1.6756\n",
      "Epoch[10/15], Step [30/469], Reconst Loss: 145.7111, KL Div: 1.5550\n",
      "Epoch[10/15], Step [40/469], Reconst Loss: 150.8427, KL Div: 1.5986\n",
      "Epoch[10/15], Step [50/469], Reconst Loss: 143.7261, KL Div: 1.5422\n",
      "Epoch[10/15], Step [60/469], Reconst Loss: 144.4471, KL Div: 1.5512\n",
      "Epoch[10/15], Step [70/469], Reconst Loss: 139.2098, KL Div: 1.5768\n",
      "Epoch[10/15], Step [80/469], Reconst Loss: 141.3239, KL Div: 1.5403\n",
      "Epoch[10/15], Step [90/469], Reconst Loss: 142.9724, KL Div: 1.6330\n",
      "Epoch[10/15], Step [100/469], Reconst Loss: 142.2347, KL Div: 1.6108\n",
      "Epoch[10/15], Step [110/469], Reconst Loss: 137.0572, KL Div: 1.6133\n",
      "Epoch[10/15], Step [120/469], Reconst Loss: 141.5999, KL Div: 1.6722\n",
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      "Epoch[10/15], Step [200/469], Reconst Loss: 148.1290, KL Div: 1.6443\n",
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      "Epoch[10/15], Step [220/469], Reconst Loss: 137.7945, KL Div: 1.6659\n",
      "Epoch[10/15], Step [230/469], Reconst Loss: 144.4710, KL Div: 1.5796\n",
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      "Epoch[10/15], Step [260/469], Reconst Loss: 145.2401, KL Div: 1.5126\n",
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      "Epoch[10/15], Step [280/469], Reconst Loss: 137.7690, KL Div: 1.5846\n",
      "Epoch[10/15], Step [290/469], Reconst Loss: 143.7475, KL Div: 1.5882\n",
      "Epoch[10/15], Step [300/469], Reconst Loss: 141.5462, KL Div: 1.5501\n",
      "Epoch[10/15], Step [310/469], Reconst Loss: 143.4236, KL Div: 1.5581\n",
      "Epoch[10/15], Step [320/469], Reconst Loss: 150.0307, KL Div: 1.6155\n",
      "Epoch[10/15], Step [330/469], Reconst Loss: 138.4069, KL Div: 1.6292\n",
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      "Epoch[10/15], Step [360/469], Reconst Loss: 144.2775, KL Div: 1.5648\n",
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      "Epoch[11/15], Step [10/469], Reconst Loss: 137.4909, KL Div: 1.5718\n",
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      "Epoch[11/15], Step [40/469], Reconst Loss: 138.8644, KL Div: 1.5722\n",
      "Epoch[11/15], Step [50/469], Reconst Loss: 140.1163, KL Div: 1.5777\n",
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      "Epoch[11/15], Step [70/469], Reconst Loss: 145.8681, KL Div: 1.7380\n",
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      "Epoch[11/15], Step [90/469], Reconst Loss: 144.7779, KL Div: 1.6565\n",
      "Epoch[11/15], Step [100/469], Reconst Loss: 138.1880, KL Div: 1.5998\n",
      "Epoch[11/15], Step [110/469], Reconst Loss: 146.2054, KL Div: 1.6261\n",
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      "Epoch[11/15], Step [260/469], Reconst Loss: 140.0292, KL Div: 1.5194\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[11/15], Step [270/469], Reconst Loss: 145.6530, KL Div: 1.5891\n",
      "Epoch[11/15], Step [280/469], Reconst Loss: 138.4326, KL Div: 1.7324\n",
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      "Epoch[11/15], Step [300/469], Reconst Loss: 138.3309, KL Div: 1.5103\n",
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      "Epoch[11/15], Step [330/469], Reconst Loss: 145.8208, KL Div: 1.7128\n",
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      "Epoch[11/15], Step [360/469], Reconst Loss: 141.2029, KL Div: 1.6107\n",
      "Epoch[11/15], Step [370/469], Reconst Loss: 148.7469, KL Div: 1.6714\n",
      "Epoch[11/15], Step [380/469], Reconst Loss: 143.7785, KL Div: 1.5785\n",
      "Epoch[11/15], Step [390/469], Reconst Loss: 139.1592, KL Div: 1.5885\n",
      "Epoch[11/15], Step [400/469], Reconst Loss: 144.0421, KL Div: 1.7654\n",
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      "Epoch[11/15], Step [440/469], Reconst Loss: 143.7670, KL Div: 1.7691\n",
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      "Epoch[11/15], Step [460/469], Reconst Loss: 137.3832, KL Div: 1.5279\n",
      "Epoch[12/15], Step [10/469], Reconst Loss: 143.7659, KL Div: 1.5953\n",
      "Epoch[12/15], Step [20/469], Reconst Loss: 143.5114, KL Div: 1.7235\n",
      "Epoch[12/15], Step [30/469], Reconst Loss: 148.1986, KL Div: 1.6874\n",
      "Epoch[12/15], Step [40/469], Reconst Loss: 152.3535, KL Div: 1.6422\n",
      "Epoch[12/15], Step [50/469], Reconst Loss: 144.9030, KL Div: 1.5774\n",
      "Epoch[12/15], Step [60/469], Reconst Loss: 146.0259, KL Div: 1.6262\n",
      "Epoch[12/15], Step [70/469], Reconst Loss: 138.8610, KL Div: 1.6457\n",
      "Epoch[12/15], Step [80/469], Reconst Loss: 135.9805, KL Div: 1.5745\n",
      "Epoch[12/15], Step [90/469], Reconst Loss: 142.7750, KL Div: 1.6261\n",
      "Epoch[12/15], Step [100/469], Reconst Loss: 139.4427, KL Div: 1.6478\n",
      "Epoch[12/15], Step [110/469], Reconst Loss: 146.5770, KL Div: 1.7968\n",
      "Epoch[12/15], Step [120/469], Reconst Loss: 142.2388, KL Div: 1.7266\n",
      "Epoch[12/15], Step [130/469], Reconst Loss: 145.5951, KL Div: 1.7265\n",
      "Epoch[12/15], Step [140/469], Reconst Loss: 133.1467, KL Div: 1.5574\n",
      "Epoch[12/15], Step [150/469], Reconst Loss: 145.6974, KL Div: 1.7109\n",
      "Epoch[12/15], Step [160/469], Reconst Loss: 148.6926, KL Div: 1.6475\n",
      "Epoch[12/15], Step [170/469], Reconst Loss: 144.3981, KL Div: 1.7380\n",
      "Epoch[12/15], Step [180/469], Reconst Loss: 137.0757, KL Div: 1.7253\n",
      "Epoch[12/15], Step [190/469], Reconst Loss: 149.5551, KL Div: 1.8295\n",
      "Epoch[12/15], Step [200/469], Reconst Loss: 143.0720, KL Div: 1.6440\n",
      "Epoch[12/15], Step [210/469], Reconst Loss: 143.1073, KL Div: 1.8539\n",
      "Epoch[12/15], Step [220/469], Reconst Loss: 137.7246, KL Div: 1.6564\n",
      "Epoch[12/15], Step [230/469], Reconst Loss: 142.8917, KL Div: 1.5939\n",
      "Epoch[12/15], Step [240/469], Reconst Loss: 145.8782, KL Div: 1.7427\n",
      "Epoch[12/15], Step [250/469], Reconst Loss: 142.3064, KL Div: 1.7737\n",
      "Epoch[12/15], Step [260/469], Reconst Loss: 147.0141, KL Div: 1.6094\n",
      "Epoch[12/15], Step [270/469], Reconst Loss: 149.2252, KL Div: 1.7775\n",
      "Epoch[12/15], Step [280/469], Reconst Loss: 152.7408, KL Div: 1.8726\n",
      "Epoch[12/15], Step [290/469], Reconst Loss: 138.0228, KL Div: 1.7491\n",
      "Epoch[12/15], Step [300/469], Reconst Loss: 141.1286, KL Div: 1.6179\n",
      "Epoch[12/15], Step [310/469], Reconst Loss: 134.7650, KL Div: 1.7079\n",
      "Epoch[12/15], Step [320/469], Reconst Loss: 142.1492, KL Div: 1.7196\n",
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      "Epoch[12/15], Step [370/469], Reconst Loss: 145.2925, KL Div: 1.8728\n",
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      "Epoch[12/15], Step [390/469], Reconst Loss: 145.2554, KL Div: 1.7629\n",
      "Epoch[12/15], Step [400/469], Reconst Loss: 140.0362, KL Div: 1.6883\n",
      "Epoch[12/15], Step [410/469], Reconst Loss: 150.5141, KL Div: 1.5916\n",
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      "Epoch[13/15], Step [10/469], Reconst Loss: 140.4519, KL Div: 1.7474\n",
      "Epoch[13/15], Step [20/469], Reconst Loss: 144.7490, KL Div: 1.7653\n",
      "Epoch[13/15], Step [30/469], Reconst Loss: 140.4178, KL Div: 1.6548\n",
      "Epoch[13/15], Step [40/469], Reconst Loss: 142.2006, KL Div: 1.6740\n",
      "Epoch[13/15], Step [50/469], Reconst Loss: 143.0444, KL Div: 1.5628\n",
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      "Epoch[13/15], Step [70/469], Reconst Loss: 134.4592, KL Div: 1.7418\n",
      "Epoch[13/15], Step [80/469], Reconst Loss: 145.1186, KL Div: 1.6778\n",
      "Epoch[13/15], Step [90/469], Reconst Loss: 147.3043, KL Div: 1.6049\n",
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      "Epoch[13/15], Step [120/469], Reconst Loss: 139.7971, KL Div: 1.6474\n",
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      "Epoch[13/15], Step [190/469], Reconst Loss: 142.9912, KL Div: 1.8760\n",
      "Epoch[13/15], Step [200/469], Reconst Loss: 145.5447, KL Div: 1.7894\n",
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      "Epoch[13/15], Step [220/469], Reconst Loss: 141.2061, KL Div: 1.6677\n",
      "Epoch[13/15], Step [230/469], Reconst Loss: 142.4634, KL Div: 1.6807\n",
      "Epoch[13/15], Step [240/469], Reconst Loss: 140.1229, KL Div: 1.6936\n",
      "Epoch[13/15], Step [250/469], Reconst Loss: 148.5001, KL Div: 1.8053\n",
      "Epoch[13/15], Step [260/469], Reconst Loss: 146.0643, KL Div: 1.7138\n",
      "Epoch[13/15], Step [270/469], Reconst Loss: 144.9190, KL Div: 1.6360\n",
      "Epoch[13/15], Step [280/469], Reconst Loss: 135.6886, KL Div: 1.6899\n",
      "Epoch[13/15], Step [290/469], Reconst Loss: 139.1357, KL Div: 1.8602\n",
      "Epoch[13/15], Step [300/469], Reconst Loss: 138.0359, KL Div: 1.6732\n",
      "Epoch[13/15], Step [310/469], Reconst Loss: 138.8120, KL Div: 1.6565\n",
      "Epoch[13/15], Step [320/469], Reconst Loss: 146.5866, KL Div: 1.7264\n",
      "Epoch[13/15], Step [330/469], Reconst Loss: 142.5253, KL Div: 1.7351\n",
      "Epoch[13/15], Step [340/469], Reconst Loss: 139.1930, KL Div: 1.6194\n",
      "Epoch[13/15], Step [350/469], Reconst Loss: 146.3159, KL Div: 1.7698\n",
      "Epoch[13/15], Step [360/469], Reconst Loss: 142.5251, KL Div: 1.8482\n",
      "Epoch[13/15], Step [370/469], Reconst Loss: 142.6813, KL Div: 1.6727\n",
      "Epoch[13/15], Step [380/469], Reconst Loss: 142.2215, KL Div: 1.7654\n",
      "Epoch[13/15], Step [390/469], Reconst Loss: 143.5160, KL Div: 1.6688\n",
      "Epoch[13/15], Step [400/469], Reconst Loss: 143.7250, KL Div: 1.6694\n",
      "Epoch[13/15], Step [410/469], Reconst Loss: 138.6320, KL Div: 1.6895\n",
      "Epoch[13/15], Step [420/469], Reconst Loss: 137.2471, KL Div: 1.7302\n",
      "Epoch[13/15], Step [430/469], Reconst Loss: 143.3463, KL Div: 1.5857\n",
      "Epoch[13/15], Step [440/469], Reconst Loss: 146.3602, KL Div: 1.7495\n",
      "Epoch[13/15], Step [450/469], Reconst Loss: 143.3869, KL Div: 1.8463\n",
      "Epoch[13/15], Step [460/469], Reconst Loss: 141.1311, KL Div: 1.7897\n",
      "Epoch[14/15], Step [10/469], Reconst Loss: 137.8118, KL Div: 1.7933\n",
      "Epoch[14/15], Step [20/469], Reconst Loss: 136.7178, KL Div: 1.6927\n",
      "Epoch[14/15], Step [30/469], Reconst Loss: 139.4101, KL Div: 1.8286\n",
      "Epoch[14/15], Step [40/469], Reconst Loss: 142.6262, KL Div: 1.6026\n",
      "Epoch[14/15], Step [50/469], Reconst Loss: 135.9612, KL Div: 1.6710\n",
      "Epoch[14/15], Step [60/469], Reconst Loss: 139.3343, KL Div: 1.7623\n",
      "Epoch[14/15], Step [70/469], Reconst Loss: 139.1727, KL Div: 1.7195\n",
      "Epoch[14/15], Step [80/469], Reconst Loss: 143.3066, KL Div: 1.9061\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[14/15], Step [90/469], Reconst Loss: 137.9312, KL Div: 1.6625\n",
      "Epoch[14/15], Step [100/469], Reconst Loss: 142.8049, KL Div: 1.8108\n",
      "Epoch[14/15], Step [110/469], Reconst Loss: 145.4223, KL Div: 1.7322\n",
      "Epoch[14/15], Step [120/469], Reconst Loss: 146.0563, KL Div: 1.7467\n",
      "Epoch[14/15], Step [130/469], Reconst Loss: 138.2344, KL Div: 1.7980\n",
      "Epoch[14/15], Step [140/469], Reconst Loss: 140.7261, KL Div: 1.7777\n",
      "Epoch[14/15], Step [150/469], Reconst Loss: 142.4458, KL Div: 1.9039\n",
      "Epoch[14/15], Step [160/469], Reconst Loss: 140.8967, KL Div: 1.6088\n",
      "Epoch[14/15], Step [170/469], Reconst Loss: 140.8730, KL Div: 1.9564\n",
      "Epoch[14/15], Step [180/469], Reconst Loss: 142.5399, KL Div: 1.7141\n",
      "Epoch[14/15], Step [190/469], Reconst Loss: 126.4016, KL Div: 1.5280\n",
      "Epoch[14/15], Step [200/469], Reconst Loss: 143.9468, KL Div: 1.8023\n",
      "Epoch[14/15], Step [210/469], Reconst Loss: 140.4802, KL Div: 1.7634\n",
      "Epoch[14/15], Step [220/469], Reconst Loss: 141.8518, KL Div: 1.7324\n",
      "Epoch[14/15], Step [230/469], Reconst Loss: 136.0516, KL Div: 1.7499\n",
      "Epoch[14/15], Step [240/469], Reconst Loss: 139.0743, KL Div: 1.9488\n",
      "Epoch[14/15], Step [250/469], Reconst Loss: 140.6410, KL Div: 1.8212\n",
      "Epoch[14/15], Step [260/469], Reconst Loss: 143.9399, KL Div: 1.6868\n",
      "Epoch[14/15], Step [270/469], Reconst Loss: 133.9317, KL Div: 1.6522\n",
      "Epoch[14/15], Step [280/469], Reconst Loss: 139.9658, KL Div: 1.8560\n",
      "Epoch[14/15], Step [290/469], Reconst Loss: 143.5783, KL Div: 1.8056\n",
      "Epoch[14/15], Step [300/469], Reconst Loss: 139.8748, KL Div: 1.7670\n",
      "Epoch[14/15], Step [310/469], Reconst Loss: 143.3349, KL Div: 1.8026\n",
      "Epoch[14/15], Step [320/469], Reconst Loss: 141.4815, KL Div: 1.8188\n",
      "Epoch[14/15], Step [330/469], Reconst Loss: 138.8896, KL Div: 1.9054\n",
      "Epoch[14/15], Step [340/469], Reconst Loss: 138.3872, KL Div: 1.6338\n",
      "Epoch[14/15], Step [350/469], Reconst Loss: 140.8717, KL Div: 1.7418\n",
      "Epoch[14/15], Step [360/469], Reconst Loss: 140.6557, KL Div: 1.8161\n",
      "Epoch[14/15], Step [370/469], Reconst Loss: 139.6850, KL Div: 1.7293\n",
      "Epoch[14/15], Step [380/469], Reconst Loss: 143.5197, KL Div: 1.6582\n",
      "Epoch[14/15], Step [390/469], Reconst Loss: 134.7218, KL Div: 1.8719\n",
      "Epoch[14/15], Step [400/469], Reconst Loss: 147.0669, KL Div: 1.8394\n",
      "Epoch[14/15], Step [410/469], Reconst Loss: 137.1610, KL Div: 1.8807\n",
      "Epoch[14/15], Step [420/469], Reconst Loss: 143.4425, KL Div: 1.8876\n",
      "Epoch[14/15], Step [430/469], Reconst Loss: 141.2787, KL Div: 1.7327\n",
      "Epoch[14/15], Step [440/469], Reconst Loss: 138.0959, KL Div: 1.6805\n",
      "Epoch[14/15], Step [450/469], Reconst Loss: 135.3143, KL Div: 1.7551\n",
      "Epoch[14/15], Step [460/469], Reconst Loss: 146.2251, KL Div: 1.7930\n",
      "Epoch[15/15], Step [10/469], Reconst Loss: 139.8721, KL Div: 1.7742\n",
      "Epoch[15/15], Step [20/469], Reconst Loss: 140.3372, KL Div: 1.8239\n",
      "Epoch[15/15], Step [30/469], Reconst Loss: 141.9252, KL Div: 1.7932\n",
      "Epoch[15/15], Step [40/469], Reconst Loss: 140.7746, KL Div: 1.7838\n",
      "Epoch[15/15], Step [50/469], Reconst Loss: 136.4255, KL Div: 1.8363\n",
      "Epoch[15/15], Step [60/469], Reconst Loss: 142.1751, KL Div: 1.7035\n",
      "Epoch[15/15], Step [70/469], Reconst Loss: 137.9019, KL Div: 1.7763\n",
      "Epoch[15/15], Step [80/469], Reconst Loss: 140.7851, KL Div: 1.9112\n",
      "Epoch[15/15], Step [90/469], Reconst Loss: 145.5002, KL Div: 1.8013\n",
      "Epoch[15/15], Step [100/469], Reconst Loss: 144.4411, KL Div: 1.8708\n",
      "Epoch[15/15], Step [110/469], Reconst Loss: 141.7414, KL Div: 1.7597\n",
      "Epoch[15/15], Step [120/469], Reconst Loss: 138.2588, KL Div: 1.7040\n",
      "Epoch[15/15], Step [130/469], Reconst Loss: 143.4338, KL Div: 1.8462\n",
      "Epoch[15/15], Step [140/469], Reconst Loss: 135.3164, KL Div: 1.7714\n",
      "Epoch[15/15], Step [150/469], Reconst Loss: 133.2934, KL Div: 1.8255\n",
      "Epoch[15/15], Step [160/469], Reconst Loss: 138.7933, KL Div: 1.8513\n",
      "Epoch[15/15], Step [170/469], Reconst Loss: 135.5251, KL Div: 1.7659\n",
      "Epoch[15/15], Step [180/469], Reconst Loss: 142.2119, KL Div: 1.7292\n",
      "Epoch[15/15], Step [190/469], Reconst Loss: 130.3994, KL Div: 1.6994\n",
      "Epoch[15/15], Step [200/469], Reconst Loss: 138.2932, KL Div: 1.7842\n",
      "Epoch[15/15], Step [210/469], Reconst Loss: 138.4194, KL Div: 1.8138\n",
      "Epoch[15/15], Step [220/469], Reconst Loss: 143.7299, KL Div: 1.8829\n",
      "Epoch[15/15], Step [230/469], Reconst Loss: 141.4182, KL Div: 1.8328\n",
      "Epoch[15/15], Step [240/469], Reconst Loss: 134.8817, KL Div: 1.8102\n",
      "Epoch[15/15], Step [250/469], Reconst Loss: 139.9543, KL Div: 1.7904\n",
      "Epoch[15/15], Step [260/469], Reconst Loss: 146.5739, KL Div: 1.6925\n",
      "Epoch[15/15], Step [270/469], Reconst Loss: 141.4621, KL Div: 1.8420\n",
      "Epoch[15/15], Step [280/469], Reconst Loss: 138.5601, KL Div: 1.8455\n",
      "Epoch[15/15], Step [290/469], Reconst Loss: 146.3525, KL Div: 2.0173\n",
      "Epoch[15/15], Step [300/469], Reconst Loss: 145.2707, KL Div: 1.7542\n",
      "Epoch[15/15], Step [310/469], Reconst Loss: 142.3568, KL Div: 1.8970\n",
      "Epoch[15/15], Step [320/469], Reconst Loss: 138.2158, KL Div: 1.8000\n",
      "Epoch[15/15], Step [330/469], Reconst Loss: 142.8834, KL Div: 1.8441\n",
      "Epoch[15/15], Step [340/469], Reconst Loss: 144.4831, KL Div: 1.8309\n",
      "Epoch[15/15], Step [350/469], Reconst Loss: 136.3605, KL Div: 1.7094\n",
      "Epoch[15/15], Step [360/469], Reconst Loss: 140.9003, KL Div: 1.6864\n",
      "Epoch[15/15], Step [370/469], Reconst Loss: 140.2641, KL Div: 1.8911\n",
      "Epoch[15/15], Step [380/469], Reconst Loss: 145.6679, KL Div: 1.9951\n",
      "Epoch[15/15], Step [390/469], Reconst Loss: 141.7043, KL Div: 1.7661\n",
      "Epoch[15/15], Step [400/469], Reconst Loss: 141.5111, KL Div: 1.9134\n",
      "Epoch[15/15], Step [410/469], Reconst Loss: 145.9840, KL Div: 1.6633\n",
      "Epoch[15/15], Step [420/469], Reconst Loss: 140.9962, KL Div: 1.8811\n",
      "Epoch[15/15], Step [430/469], Reconst Loss: 137.4240, KL Div: 1.7128\n",
      "Epoch[15/15], Step [440/469], Reconst Loss: 132.4869, KL Div: 1.8263\n",
      "Epoch[15/15], Step [450/469], Reconst Loss: 144.4298, KL Div: 1.9462\n",
      "Epoch[15/15], Step [460/469], Reconst Loss: 137.5294, KL Div: 1.7028\n"
     ]
    }
   ],
   "source": [
    "train_C(model_C,num_epochs=15,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conditional_generation(model_C, n=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you should get nice results. Now we will avoid the use of the labels..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ePpthCZXMtyS"
   },
   "source": [
    "# No cheating with Gumbel VAE\n",
    "\n",
    "Implement a VAE where you add a categorical variable $c\\in \\{0,\\dots 9\\}$ so that your latent variable model is $p(c,z,x) = p(c)p(z)p_{\\theta}(x|c,z)$ and your variational posterior is $q_{\\phi}(c|x)q_{\\phi}(z|x)$ as described in this NeurIPS paper: [(Dupont, 2018)](https://arxiv.org/abs/1804.00104). Make minimal modifications to previous architecture.\n",
    "\n",
    "The idea is to incorporate a categorical variable in your latent space. You hope that this categorical variable will encode the class of the digit, so that your network can use it for a better reconstruction. Moreover, if things work as planned, you will then be able to generate digits conditionally to the class, i.e. you can choose the class thanks to the latent categorical variable $c$ and then generate digits from this class.\n",
    "\n",
    "As noticed above, in order to sample random variables while still being able to use backpropagation, we need to use the reparameterization trick which is easy for Gaussian random variables. For categorical random variables, the reparameterization trick is explained in [(Jang et al., 2016)](https://arxiv.org/abs/1611.01144). This is implemented in PyTorch thanks to [F.gumbel_softmax](https://pytorch.org/docs/stable/nn.html?highlight=gumbel_softmax#torch.nn.functional.gumbel_softmax)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sBIKV-68MtyW"
   },
   "outputs": [],
   "source": [
    "n_classes = 10\n",
    "\n",
    "class VAE_Gumbel(nn.Module):\n",
    "    def __init__(self, image_size=784, h_dim=400, z_dim=20, n_classes = 10):\n",
    "        super(VAE_Gumbel, self).__init__()\n",
    "        self.fc1 = nn.Linear(image_size, h_dim)\n",
    "        self.fc2 = nn.Linear(h_dim, z_dim)\n",
    "        self.fc3 = nn.Linear(h_dim, z_dim)\n",
    "        self.fc4 = nn.Linear(h_dim, n_classes)\n",
    "        self.fc5 = nn.Linear(z_dim+n_classes, h_dim)\n",
    "        self.fc6 = nn.Linear(h_dim, image_size)\n",
    "        \n",
    "    def encode(self, x):\n",
    "        h = F.relu(self.fc1(x))\n",
    "        return self.fc2(h), self.fc3(h), F.log_softmax(self.fc4(h),dim=1)\n",
    "    \n",
    "    def reparameterize(self, mu, log_var):\n",
    "        std = torch.exp(log_var/2)\n",
    "        eps = torch.randn_like(std)\n",
    "        return mu + eps * std\n",
    "\n",
    "    def decode(self, z, y_onehot):\n",
    "        x = torch.cat([z, y_onehot], 1)\n",
    "        h = F.relu(self.fc5(x))\n",
    "        return torch.sigmoid(self.fc6(h))\n",
    "    \n",
    "    def forward(self, x):\n",
    "        mu, log_var, log_p = self.encode(x)\n",
    "        z = self.reparameterize(mu, log_var)\n",
    "        y_onehot = F.gumbel_softmax(log_p,tau=0.5,hard=True)\n",
    "        x_reconst = self.decode(z,y_onehot)\n",
    "        return x_reconst, mu, log_var, log_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sBIKV-68MtyW"
   },
   "outputs": [],
   "source": [
    "model_G = VAE_Gumbel().to(device)\n",
    "optimizer = torch.optim.Adam(model_G.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KykEyDKDMtyc"
   },
   "source": [
    "You need to modify the loss to take into account the categorical random variable with an uniform prior on $\\{0,\\dots 9\\}$, see Appendix A.2 in [(Dupont, 2018)](https://arxiv.org/abs/1804.00104)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JBV8OdB9Mtyg"
   },
   "outputs": [],
   "source": [
    "def train_G(model, data_loader=data_loader,num_epochs=num_epochs, beta = 1., verbose=True):\n",
    "    nmi_scores = []\n",
    "    model.train(True)\n",
    "    for epoch in range(num_epochs):\n",
    "        all_labels = []\n",
    "        all_labels_est = []\n",
    "        for i, (x, labels) in enumerate(data_loader):\n",
    "            # Forward pass\n",
    "            x = x.to(device).view(-1, image_size)\n",
    "            x_reconst, mu, log_var, log_p = model(x)\n",
    "            _,labels_est = torch.max(log_p,1)\n",
    "            all_labels += list(labels.cpu().data.numpy())\n",
    "            all_labels_est += list(labels_est.cpu().data.numpy())\n",
    "            \n",
    "            reconst_loss = F.binary_cross_entropy(x_reconst, x, reduction='sum')\n",
    "            kl_div =  - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())\n",
    "            H_cat =  -torch.sum(torch.exp(log_p)*log_p)\n",
    "\n",
    "            # Backprop and optimize\n",
    "            loss = reconst_loss + beta*(kl_div-H_cat)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            if verbose:\n",
    "                if (i+1) % 10 == 0:\n",
    "                    print (\"Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}, Entropy: {:.4f}\" \n",
    "                           .format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item()/len(x),\n",
    "                                   kl_div.item()/len(x), H_cat.item()/len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KL5mR20KMtyp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/10], Step [10/469], Reconst Loss: 298.0704, KL Div: 23.7714, Entropy: 1.9440\n",
      "Epoch[1/10], Step [20/469], Reconst Loss: 234.9258, KL Div: 9.1024, Entropy: 2.2044\n",
      "Epoch[1/10], Step [30/469], Reconst Loss: 220.9930, KL Div: 12.2237, Entropy: 2.1943\n",
      "Epoch[1/10], Step [40/469], Reconst Loss: 212.0129, KL Div: 4.7374, Entropy: 2.2804\n",
      "Epoch[1/10], Step [50/469], Reconst Loss: 201.3938, KL Div: 6.1574, Entropy: 2.2814\n",
      "Epoch[1/10], Step [60/469], Reconst Loss: 194.3774, KL Div: 7.1824, Entropy: 2.2794\n",
      "Epoch[1/10], Step [70/469], Reconst Loss: 206.2737, KL Div: 6.2790, Entropy: 2.2804\n",
      "Epoch[1/10], Step [80/469], Reconst Loss: 186.9584, KL Div: 8.7917, Entropy: 2.2744\n",
      "Epoch[1/10], Step [90/469], Reconst Loss: 188.4521, KL Div: 9.4335, Entropy: 2.2787\n",
      "Epoch[1/10], Step [100/469], Reconst Loss: 176.4370, KL Div: 10.4698, Entropy: 2.2789\n",
      "Epoch[1/10], Step [110/469], Reconst Loss: 179.0192, KL Div: 10.1710, Entropy: 2.2784\n",
      "Epoch[1/10], Step [120/469], Reconst Loss: 165.7665, KL Div: 11.7126, Entropy: 2.2775\n",
      "Epoch[1/10], Step [130/469], Reconst Loss: 158.1651, KL Div: 12.5382, Entropy: 2.2727\n",
      "Epoch[1/10], Step [140/469], Reconst Loss: 157.8618, KL Div: 12.7636, Entropy: 2.2747\n",
      "Epoch[1/10], Step [150/469], Reconst Loss: 163.8606, KL Div: 13.5821, Entropy: 2.2682\n",
      "Epoch[1/10], Step [160/469], Reconst Loss: 148.5144, KL Div: 13.5746, Entropy: 2.2697\n",
      "Epoch[1/10], Step [170/469], Reconst Loss: 153.0307, KL Div: 13.5263, Entropy: 2.2752\n",
      "Epoch[1/10], Step [180/469], Reconst Loss: 148.3149, KL Div: 14.2406, Entropy: 2.2770\n",
      "Epoch[1/10], Step [190/469], Reconst Loss: 147.1594, KL Div: 12.9045, Entropy: 2.2800\n",
      "Epoch[1/10], Step [200/469], Reconst Loss: 143.4744, KL Div: 14.4217, Entropy: 2.2740\n",
      "Epoch[1/10], Step [210/469], Reconst Loss: 138.1618, KL Div: 15.2761, Entropy: 2.2722\n",
      "Epoch[1/10], Step [220/469], Reconst Loss: 137.1660, KL Div: 15.2366, Entropy: 2.2761\n",
      "Epoch[1/10], Step [230/469], Reconst Loss: 136.0045, KL Div: 15.5169, Entropy: 2.2775\n",
      "Epoch[1/10], Step [240/469], Reconst Loss: 128.9195, KL Div: 16.1423, Entropy: 2.2768\n",
      "Epoch[1/10], Step [250/469], Reconst Loss: 127.0508, KL Div: 16.4723, Entropy: 2.2792\n",
      "Epoch[1/10], Step [260/469], Reconst Loss: 130.1539, KL Div: 16.6497, Entropy: 2.2830\n",
      "Epoch[1/10], Step [270/469], Reconst Loss: 125.1858, KL Div: 16.5771, Entropy: 2.2819\n",
      "Epoch[1/10], Step [280/469], Reconst Loss: 127.5710, KL Div: 16.5908, Entropy: 2.2819\n",
      "Epoch[1/10], Step [290/469], Reconst Loss: 124.8757, KL Div: 16.8056, Entropy: 2.2763\n",
      "Epoch[1/10], Step [300/469], Reconst Loss: 122.2487, KL Div: 16.5593, Entropy: 2.2811\n",
      "Epoch[1/10], Step [310/469], Reconst Loss: 120.7441, KL Div: 17.9879, Entropy: 2.2814\n",
      "Epoch[1/10], Step [320/469], Reconst Loss: 122.4272, KL Div: 18.2870, Entropy: 2.2818\n",
      "Epoch[1/10], Step [330/469], Reconst Loss: 117.0341, KL Div: 18.9085, Entropy: 2.2788\n",
      "Epoch[1/10], Step [340/469], Reconst Loss: 121.6615, KL Div: 18.6546, Entropy: 2.2808\n",
      "Epoch[1/10], Step [350/469], Reconst Loss: 118.7716, KL Div: 17.5385, Entropy: 2.2817\n",
      "Epoch[1/10], Step [360/469], Reconst Loss: 121.4232, KL Div: 18.2571, Entropy: 2.2838\n",
      "Epoch[1/10], Step [370/469], Reconst Loss: 118.3170, KL Div: 17.5253, Entropy: 2.2824\n",
      "Epoch[1/10], Step [380/469], Reconst Loss: 118.4606, KL Div: 18.4920, Entropy: 2.2762\n",
      "Epoch[1/10], Step [390/469], Reconst Loss: 122.2811, KL Div: 19.0484, Entropy: 2.2793\n",
      "Epoch[1/10], Step [400/469], Reconst Loss: 114.2935, KL Div: 18.1616, Entropy: 2.2818\n",
      "Epoch[1/10], Step [410/469], Reconst Loss: 113.0529, KL Div: 19.6827, Entropy: 2.2765\n",
      "Epoch[1/10], Step [420/469], Reconst Loss: 116.5293, KL Div: 19.2655, Entropy: 2.2753\n",
      "Epoch[1/10], Step [430/469], Reconst Loss: 114.5694, KL Div: 19.0097, Entropy: 2.2782\n",
      "Epoch[1/10], Step [440/469], Reconst Loss: 112.7698, KL Div: 19.6985, Entropy: 2.2746\n",
      "Epoch[1/10], Step [450/469], Reconst Loss: 111.0800, KL Div: 19.8130, Entropy: 2.2778\n",
      "Epoch[1/10], Step [460/469], Reconst Loss: 112.8987, KL Div: 19.9536, Entropy: 2.2790\n",
      "Epoch[2/10], Step [10/469], Reconst Loss: 107.7654, KL Div: 21.0455, Entropy: 2.2803\n",
      "Epoch[2/10], Step [20/469], Reconst Loss: 109.2456, KL Div: 20.6969, Entropy: 2.2792\n",
      "Epoch[2/10], Step [30/469], Reconst Loss: 105.8974, KL Div: 20.1350, Entropy: 2.2807\n",
      "Epoch[2/10], Step [40/469], Reconst Loss: 106.6399, KL Div: 21.8209, Entropy: 2.2790\n",
      "Epoch[2/10], Step [50/469], Reconst Loss: 105.8383, KL Div: 20.6088, Entropy: 2.2766\n",
      "Epoch[2/10], Step [60/469], Reconst Loss: 105.3336, KL Div: 20.0778, Entropy: 2.2813\n",
      "Epoch[2/10], Step [70/469], Reconst Loss: 106.3157, KL Div: 21.2933, Entropy: 2.2755\n",
      "Epoch[2/10], Step [80/469], Reconst Loss: 104.9729, KL Div: 20.2444, Entropy: 2.2821\n",
      "Epoch[2/10], Step [90/469], Reconst Loss: 103.6247, KL Div: 21.3600, Entropy: 2.2837\n",
      "Epoch[2/10], Step [100/469], Reconst Loss: 107.9060, KL Div: 19.5392, Entropy: 2.2813\n",
      "Epoch[2/10], Step [110/469], Reconst Loss: 99.8356, KL Div: 21.2802, Entropy: 2.2804\n",
      "Epoch[2/10], Step [120/469], Reconst Loss: 103.5702, KL Div: 20.8831, Entropy: 2.2839\n",
      "Epoch[2/10], Step [130/469], Reconst Loss: 100.2446, KL Div: 22.2363, Entropy: 2.2812\n",
      "Epoch[2/10], Step [140/469], Reconst Loss: 104.2060, KL Div: 21.6637, Entropy: 2.2844\n",
      "Epoch[2/10], Step [150/469], Reconst Loss: 98.2077, KL Div: 20.9272, Entropy: 2.2852\n",
      "Epoch[2/10], Step [160/469], Reconst Loss: 101.9783, KL Div: 20.7281, Entropy: 2.2874\n",
      "Epoch[2/10], Step [170/469], Reconst Loss: 102.0534, KL Div: 22.0855, Entropy: 2.2834\n",
      "Epoch[2/10], Step [180/469], Reconst Loss: 98.8168, KL Div: 21.9570, Entropy: 2.2882\n",
      "Epoch[2/10], Step [190/469], Reconst Loss: 100.1846, KL Div: 21.5917, Entropy: 2.2859\n",
      "Epoch[2/10], Step [200/469], Reconst Loss: 102.9781, KL Div: 21.4200, Entropy: 2.2887\n",
      "Epoch[2/10], Step [210/469], Reconst Loss: 103.1593, KL Div: 21.4331, Entropy: 2.2869\n",
      "Epoch[2/10], Step [220/469], Reconst Loss: 98.6049, KL Div: 22.8130, Entropy: 2.2865\n",
      "Epoch[2/10], Step [230/469], Reconst Loss: 101.6543, KL Div: 21.7781, Entropy: 2.2824\n",
      "Epoch[2/10], Step [240/469], Reconst Loss: 97.9708, KL Div: 22.1690, Entropy: 2.2877\n",
      "Epoch[2/10], Step [250/469], Reconst Loss: 106.8947, KL Div: 21.9537, Entropy: 2.2866\n",
      "Epoch[2/10], Step [260/469], Reconst Loss: 95.8055, KL Div: 22.1388, Entropy: 2.2903\n",
      "Epoch[2/10], Step [270/469], Reconst Loss: 97.0217, KL Div: 22.5133, Entropy: 2.2898\n",
      "Epoch[2/10], Step [280/469], Reconst Loss: 99.9958, KL Div: 22.3968, Entropy: 2.2868\n",
      "Epoch[2/10], Step [290/469], Reconst Loss: 91.9848, KL Div: 22.1419, Entropy: 2.2885\n",
      "Epoch[2/10], Step [300/469], Reconst Loss: 101.4254, KL Div: 22.2628, Entropy: 2.2904\n",
      "Epoch[2/10], Step [310/469], Reconst Loss: 98.6548, KL Div: 23.0585, Entropy: 2.2893\n",
      "Epoch[2/10], Step [320/469], Reconst Loss: 95.7471, KL Div: 22.8256, Entropy: 2.2907\n",
      "Epoch[2/10], Step [330/469], Reconst Loss: 97.2405, KL Div: 22.7981, Entropy: 2.2889\n",
      "Epoch[2/10], Step [340/469], Reconst Loss: 93.3668, KL Div: 22.8712, Entropy: 2.2920\n",
      "Epoch[2/10], Step [350/469], Reconst Loss: 95.7226, KL Div: 22.6023, Entropy: 2.2905\n",
      "Epoch[2/10], Step [360/469], Reconst Loss: 93.9548, KL Div: 23.0680, Entropy: 2.2927\n",
      "Epoch[2/10], Step [370/469], Reconst Loss: 94.7991, KL Div: 23.0072, Entropy: 2.2899\n",
      "Epoch[2/10], Step [380/469], Reconst Loss: 94.4841, KL Div: 22.9247, Entropy: 2.2912\n",
      "Epoch[2/10], Step [390/469], Reconst Loss: 96.9711, KL Div: 22.9970, Entropy: 2.2914\n",
      "Epoch[2/10], Step [400/469], Reconst Loss: 92.0275, KL Div: 22.6454, Entropy: 2.2928\n",
      "Epoch[2/10], Step [410/469], Reconst Loss: 95.2184, KL Div: 23.4452, Entropy: 2.2916\n",
      "Epoch[2/10], Step [420/469], Reconst Loss: 91.7258, KL Div: 23.3264, Entropy: 2.2930\n",
      "Epoch[2/10], Step [430/469], Reconst Loss: 92.1308, KL Div: 22.8075, Entropy: 2.2928\n",
      "Epoch[2/10], Step [440/469], Reconst Loss: 95.1134, KL Div: 23.1274, Entropy: 2.2921\n",
      "Epoch[2/10], Step [450/469], Reconst Loss: 94.3713, KL Div: 22.7288, Entropy: 2.2948\n",
      "Epoch[2/10], Step [460/469], Reconst Loss: 89.6932, KL Div: 22.6478, Entropy: 2.2931\n",
      "Epoch[3/10], Step [10/469], Reconst Loss: 94.7001, KL Div: 22.8673, Entropy: 2.2915\n",
      "Epoch[3/10], Step [20/469], Reconst Loss: 93.1408, KL Div: 23.8478, Entropy: 2.2917\n",
      "Epoch[3/10], Step [30/469], Reconst Loss: 95.2767, KL Div: 23.7619, Entropy: 2.2931\n",
      "Epoch[3/10], Step [40/469], Reconst Loss: 91.2488, KL Div: 23.3855, Entropy: 2.2933\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[3/10], Step [50/469], Reconst Loss: 91.8824, KL Div: 22.6599, Entropy: 2.2941\n",
      "Epoch[3/10], Step [60/469], Reconst Loss: 95.8198, KL Div: 23.5392, Entropy: 2.2937\n",
      "Epoch[3/10], Step [70/469], Reconst Loss: 94.2666, KL Div: 24.1664, Entropy: 2.2906\n",
      "Epoch[3/10], Step [80/469], Reconst Loss: 92.3231, KL Div: 23.6740, Entropy: 2.2910\n",
      "Epoch[3/10], Step [90/469], Reconst Loss: 95.9179, KL Div: 23.5361, Entropy: 2.2930\n",
      "Epoch[3/10], Step [100/469], Reconst Loss: 94.7956, KL Div: 24.2760, Entropy: 2.2925\n",
      "Epoch[3/10], Step [110/469], Reconst Loss: 94.6584, KL Div: 24.1189, Entropy: 2.2913\n",
      "Epoch[3/10], Step [120/469], Reconst Loss: 91.5363, KL Div: 22.8726, Entropy: 2.2942\n",
      "Epoch[3/10], Step [130/469], Reconst Loss: 96.3462, KL Div: 24.9154, Entropy: 2.2942\n",
      "Epoch[3/10], Step [140/469], Reconst Loss: 93.8244, KL Div: 23.5938, Entropy: 2.2936\n",
      "Epoch[3/10], Step [150/469], Reconst Loss: 91.1092, KL Div: 24.6827, Entropy: 2.2942\n",
      "Epoch[3/10], Step [160/469], Reconst Loss: 91.2352, KL Div: 24.3726, Entropy: 2.2940\n",
      "Epoch[3/10], Step [170/469], Reconst Loss: 91.7603, KL Div: 23.7764, Entropy: 2.2940\n",
      "Epoch[3/10], Step [180/469], Reconst Loss: 91.9455, KL Div: 23.9542, Entropy: 2.2945\n",
      "Epoch[3/10], Step [190/469], Reconst Loss: 90.6149, KL Div: 24.2093, Entropy: 2.2953\n",
      "Epoch[3/10], Step [200/469], Reconst Loss: 88.8895, KL Div: 23.3357, Entropy: 2.2950\n",
      "Epoch[3/10], Step [210/469], Reconst Loss: 91.5272, KL Div: 24.2209, Entropy: 2.2922\n",
      "Epoch[3/10], Step [220/469], Reconst Loss: 93.2971, KL Div: 24.1907, Entropy: 2.2927\n",
      "Epoch[3/10], Step [230/469], Reconst Loss: 90.9606, KL Div: 24.0046, Entropy: 2.2943\n",
      "Epoch[3/10], Step [240/469], Reconst Loss: 91.4429, KL Div: 24.6715, Entropy: 2.2939\n",
      "Epoch[3/10], Step [250/469], Reconst Loss: 93.6816, KL Div: 23.9834, Entropy: 2.2946\n",
      "Epoch[3/10], Step [260/469], Reconst Loss: 93.6541, KL Div: 24.3711, Entropy: 2.2961\n",
      "Epoch[3/10], Step [270/469], Reconst Loss: 89.5365, KL Div: 24.5199, Entropy: 2.2943\n",
      "Epoch[3/10], Step [280/469], Reconst Loss: 90.3264, KL Div: 23.9959, Entropy: 2.2948\n",
      "Epoch[3/10], Step [290/469], Reconst Loss: 88.5389, KL Div: 24.0511, Entropy: 2.2942\n",
      "Epoch[3/10], Step [300/469], Reconst Loss: 91.4978, KL Div: 24.2982, Entropy: 2.2939\n",
      "Epoch[3/10], Step [310/469], Reconst Loss: 92.8756, KL Div: 24.0736, Entropy: 2.2949\n",
      "Epoch[3/10], Step [320/469], Reconst Loss: 90.9833, KL Div: 24.4615, Entropy: 2.2951\n",
      "Epoch[3/10], Step [330/469], Reconst Loss: 86.2159, KL Div: 24.5572, Entropy: 2.2950\n",
      "Epoch[3/10], Step [340/469], Reconst Loss: 88.4341, KL Div: 23.8260, Entropy: 2.2953\n",
      "Epoch[3/10], Step [350/469], Reconst Loss: 91.5451, KL Div: 24.5307, Entropy: 2.2946\n",
      "Epoch[3/10], Step [360/469], Reconst Loss: 90.6203, KL Div: 23.4497, Entropy: 2.2960\n",
      "Epoch[3/10], Step [370/469], Reconst Loss: 87.4306, KL Div: 24.6270, Entropy: 2.2954\n",
      "Epoch[3/10], Step [380/469], Reconst Loss: 90.0592, KL Div: 24.3315, Entropy: 2.2942\n",
      "Epoch[3/10], Step [390/469], Reconst Loss: 91.9143, KL Div: 24.2856, Entropy: 2.2951\n",
      "Epoch[3/10], Step [400/469], Reconst Loss: 91.8251, KL Div: 24.1643, Entropy: 2.2942\n",
      "Epoch[3/10], Step [410/469], Reconst Loss: 89.5755, KL Div: 23.9819, Entropy: 2.2945\n",
      "Epoch[3/10], Step [420/469], Reconst Loss: 92.2608, KL Div: 25.3303, Entropy: 2.2950\n",
      "Epoch[3/10], Step [430/469], Reconst Loss: 89.8187, KL Div: 24.3067, Entropy: 2.2958\n",
      "Epoch[3/10], Step [440/469], Reconst Loss: 86.7153, KL Div: 23.7874, Entropy: 2.2946\n",
      "Epoch[3/10], Step [450/469], Reconst Loss: 87.0062, KL Div: 24.2532, Entropy: 2.2954\n",
      "Epoch[3/10], Step [460/469], Reconst Loss: 88.3153, KL Div: 23.7341, Entropy: 2.2951\n",
      "Epoch[4/10], Step [10/469], Reconst Loss: 86.8071, KL Div: 23.4276, Entropy: 2.2942\n",
      "Epoch[4/10], Step [20/469], Reconst Loss: 85.6570, KL Div: 24.2524, Entropy: 2.2944\n",
      "Epoch[4/10], Step [30/469], Reconst Loss: 89.9094, KL Div: 25.1437, Entropy: 2.2951\n",
      "Epoch[4/10], Step [40/469], Reconst Loss: 87.0083, KL Div: 24.4184, Entropy: 2.2947\n",
      "Epoch[4/10], Step [50/469], Reconst Loss: 87.9922, KL Div: 23.9944, Entropy: 2.2944\n",
      "Epoch[4/10], Step [60/469], Reconst Loss: 86.9343, KL Div: 24.9761, Entropy: 2.2953\n",
      "Epoch[4/10], Step [70/469], Reconst Loss: 87.9356, KL Div: 24.1046, Entropy: 2.2959\n",
      "Epoch[4/10], Step [80/469], Reconst Loss: 87.3127, KL Div: 24.5818, Entropy: 2.2968\n",
      "Epoch[4/10], Step [90/469], Reconst Loss: 86.7610, KL Div: 24.8001, Entropy: 2.2951\n",
      "Epoch[4/10], Step [100/469], Reconst Loss: 88.3575, KL Div: 25.1777, Entropy: 2.2943\n",
      "Epoch[4/10], Step [110/469], Reconst Loss: 87.5767, KL Div: 24.2018, Entropy: 2.2947\n",
      "Epoch[4/10], Step [120/469], Reconst Loss: 86.0470, KL Div: 24.4041, Entropy: 2.2937\n",
      "Epoch[4/10], Step [130/469], Reconst Loss: 87.1273, KL Div: 24.5660, Entropy: 2.2934\n",
      "Epoch[4/10], Step [140/469], Reconst Loss: 86.7514, KL Div: 23.8249, Entropy: 2.2957\n",
      "Epoch[4/10], Step [150/469], Reconst Loss: 85.5057, KL Div: 24.4438, Entropy: 2.2954\n",
      "Epoch[4/10], Step [160/469], Reconst Loss: 86.5666, KL Div: 23.6148, Entropy: 2.2952\n",
      "Epoch[4/10], Step [170/469], Reconst Loss: 87.2398, KL Div: 24.6978, Entropy: 2.2967\n",
      "Epoch[4/10], Step [180/469], Reconst Loss: 85.1971, KL Div: 23.7884, Entropy: 2.2963\n",
      "Epoch[4/10], Step [190/469], Reconst Loss: 88.1935, KL Div: 24.3422, Entropy: 2.2965\n",
      "Epoch[4/10], Step [200/469], Reconst Loss: 83.0842, KL Div: 25.0593, Entropy: 2.2969\n",
      "Epoch[4/10], Step [210/469], Reconst Loss: 89.3716, KL Div: 24.1983, Entropy: 2.2968\n",
      "Epoch[4/10], Step [220/469], Reconst Loss: 86.7692, KL Div: 24.9390, Entropy: 2.2972\n",
      "Epoch[4/10], Step [230/469], Reconst Loss: 85.0523, KL Div: 22.9192, Entropy: 2.2968\n",
      "Epoch[4/10], Step [240/469], Reconst Loss: 89.8620, KL Div: 24.6837, Entropy: 2.2966\n",
      "Epoch[4/10], Step [250/469], Reconst Loss: 91.2358, KL Div: 25.0752, Entropy: 2.2954\n",
      "Epoch[4/10], Step [260/469], Reconst Loss: 84.8650, KL Div: 24.0912, Entropy: 2.2977\n",
      "Epoch[4/10], Step [270/469], Reconst Loss: 84.6027, KL Div: 24.1328, Entropy: 2.2972\n",
      "Epoch[4/10], Step [280/469], Reconst Loss: 90.6183, KL Div: 24.3123, Entropy: 2.2967\n",
      "Epoch[4/10], Step [290/469], Reconst Loss: 90.6742, KL Div: 25.4845, Entropy: 2.2961\n",
      "Epoch[4/10], Step [300/469], Reconst Loss: 86.7997, KL Div: 25.0960, Entropy: 2.2962\n",
      "Epoch[4/10], Step [310/469], Reconst Loss: 90.3307, KL Div: 24.8090, Entropy: 2.2958\n",
      "Epoch[4/10], Step [320/469], Reconst Loss: 84.0921, KL Div: 24.8451, Entropy: 2.2969\n",
      "Epoch[4/10], Step [330/469], Reconst Loss: 86.9849, KL Div: 24.7491, Entropy: 2.2962\n",
      "Epoch[4/10], Step [340/469], Reconst Loss: 85.9921, KL Div: 24.4957, Entropy: 2.2967\n",
      "Epoch[4/10], Step [350/469], Reconst Loss: 82.9102, KL Div: 24.2399, Entropy: 2.2969\n",
      "Epoch[4/10], Step [360/469], Reconst Loss: 86.3741, KL Div: 24.8891, Entropy: 2.2963\n",
      "Epoch[4/10], Step [370/469], Reconst Loss: 83.6621, KL Div: 23.6297, Entropy: 2.2959\n",
      "Epoch[4/10], Step [380/469], Reconst Loss: 87.2634, KL Div: 24.6638, Entropy: 2.2946\n",
      "Epoch[4/10], Step [390/469], Reconst Loss: 85.2137, KL Div: 24.1388, Entropy: 2.2962\n",
      "Epoch[4/10], Step [400/469], Reconst Loss: 90.5846, KL Div: 24.7868, Entropy: 2.2958\n",
      "Epoch[4/10], Step [410/469], Reconst Loss: 87.2036, KL Div: 24.2040, Entropy: 2.2969\n",
      "Epoch[4/10], Step [420/469], Reconst Loss: 85.3766, KL Div: 24.4124, Entropy: 2.2972\n",
      "Epoch[4/10], Step [430/469], Reconst Loss: 82.1504, KL Div: 24.9130, Entropy: 2.2967\n",
      "Epoch[4/10], Step [440/469], Reconst Loss: 87.2674, KL Div: 24.2053, Entropy: 2.2967\n",
      "Epoch[4/10], Step [450/469], Reconst Loss: 83.7576, KL Div: 24.8263, Entropy: 2.2970\n",
      "Epoch[4/10], Step [460/469], Reconst Loss: 87.2000, KL Div: 25.1905, Entropy: 2.2969\n",
      "Epoch[5/10], Step [10/469], Reconst Loss: 87.3627, KL Div: 24.3495, Entropy: 2.2970\n",
      "Epoch[5/10], Step [20/469], Reconst Loss: 83.7279, KL Div: 24.2596, Entropy: 2.2974\n",
      "Epoch[5/10], Step [30/469], Reconst Loss: 84.8796, KL Div: 25.0393, Entropy: 2.2958\n",
      "Epoch[5/10], Step [40/469], Reconst Loss: 87.9300, KL Div: 24.4374, Entropy: 2.2961\n",
      "Epoch[5/10], Step [50/469], Reconst Loss: 87.4260, KL Div: 25.2960, Entropy: 2.2962\n",
      "Epoch[5/10], Step [60/469], Reconst Loss: 84.8067, KL Div: 24.7843, Entropy: 2.2962\n",
      "Epoch[5/10], Step [70/469], Reconst Loss: 85.1340, KL Div: 24.3406, Entropy: 2.2973\n",
      "Epoch[5/10], Step [80/469], Reconst Loss: 84.9143, KL Div: 24.4680, Entropy: 2.2971\n",
      "Epoch[5/10], Step [90/469], Reconst Loss: 86.2962, KL Div: 24.3523, Entropy: 2.2967\n",
      "Epoch[5/10], Step [100/469], Reconst Loss: 87.1572, KL Div: 25.0675, Entropy: 2.2963\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[5/10], Step [110/469], Reconst Loss: 87.3471, KL Div: 24.6969, Entropy: 2.2963\n",
      "Epoch[5/10], Step [120/469], Reconst Loss: 82.4907, KL Div: 25.1968, Entropy: 2.2963\n",
      "Epoch[5/10], Step [130/469], Reconst Loss: 86.1535, KL Div: 24.8392, Entropy: 2.2955\n",
      "Epoch[5/10], Step [140/469], Reconst Loss: 88.0293, KL Div: 24.8179, Entropy: 2.2966\n",
      "Epoch[5/10], Step [150/469], Reconst Loss: 89.1142, KL Div: 25.6882, Entropy: 2.2963\n",
      "Epoch[5/10], Step [160/469], Reconst Loss: 86.6520, KL Div: 25.1382, Entropy: 2.2955\n",
      "Epoch[5/10], Step [170/469], Reconst Loss: 85.7772, KL Div: 24.7284, Entropy: 2.2969\n",
      "Epoch[5/10], Step [180/469], Reconst Loss: 84.5806, KL Div: 23.9590, Entropy: 2.2964\n",
      "Epoch[5/10], Step [190/469], Reconst Loss: 91.7940, KL Div: 25.4847, Entropy: 2.2963\n",
      "Epoch[5/10], Step [200/469], Reconst Loss: 86.1052, KL Div: 24.7648, Entropy: 2.2965\n",
      "Epoch[5/10], Step [210/469], Reconst Loss: 88.9397, KL Div: 24.3610, Entropy: 2.2966\n",
      "Epoch[5/10], Step [220/469], Reconst Loss: 83.1924, KL Div: 24.7008, Entropy: 2.2969\n",
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      "Epoch[5/10], Step [290/469], Reconst Loss: 83.1654, KL Div: 24.8931, Entropy: 2.2974\n",
      "Epoch[5/10], Step [300/469], Reconst Loss: 81.1987, KL Div: 23.2096, Entropy: 2.2977\n",
      "Epoch[5/10], Step [310/469], Reconst Loss: 86.6877, KL Div: 25.1014, Entropy: 2.2968\n",
      "Epoch[5/10], Step [320/469], Reconst Loss: 80.8880, KL Div: 24.5460, Entropy: 2.2975\n",
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      "Epoch[5/10], Step [370/469], Reconst Loss: 84.1885, KL Div: 24.2484, Entropy: 2.2977\n",
      "Epoch[5/10], Step [380/469], Reconst Loss: 86.1835, KL Div: 25.3024, Entropy: 2.2968\n",
      "Epoch[5/10], Step [390/469], Reconst Loss: 85.0775, KL Div: 24.4436, Entropy: 2.2978\n",
      "Epoch[5/10], Step [400/469], Reconst Loss: 81.4005, KL Div: 25.2339, Entropy: 2.2976\n",
      "Epoch[5/10], Step [410/469], Reconst Loss: 80.8873, KL Div: 24.0690, Entropy: 2.2976\n",
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      "Epoch[5/10], Step [430/469], Reconst Loss: 85.1053, KL Div: 24.9055, Entropy: 2.2978\n",
      "Epoch[5/10], Step [440/469], Reconst Loss: 81.9470, KL Div: 24.1997, Entropy: 2.2975\n",
      "Epoch[5/10], Step [450/469], Reconst Loss: 81.1252, KL Div: 24.3293, Entropy: 2.2976\n",
      "Epoch[5/10], Step [460/469], Reconst Loss: 85.9344, KL Div: 24.9758, Entropy: 2.2966\n",
      "Epoch[6/10], Step [10/469], Reconst Loss: 85.2547, KL Div: 25.5594, Entropy: 2.2967\n",
      "Epoch[6/10], Step [20/469], Reconst Loss: 82.6419, KL Div: 24.5570, Entropy: 2.2970\n",
      "Epoch[6/10], Step [30/469], Reconst Loss: 84.8404, KL Div: 24.7737, Entropy: 2.2973\n",
      "Epoch[6/10], Step [40/469], Reconst Loss: 87.9280, KL Div: 25.1067, Entropy: 2.2973\n",
      "Epoch[6/10], Step [50/469], Reconst Loss: 80.2591, KL Div: 24.3286, Entropy: 2.2976\n",
      "Epoch[6/10], Step [60/469], Reconst Loss: 85.3031, KL Div: 25.1170, Entropy: 2.2978\n",
      "Epoch[6/10], Step [70/469], Reconst Loss: 85.2400, KL Div: 24.9047, Entropy: 2.2979\n",
      "Epoch[6/10], Step [80/469], Reconst Loss: 89.4707, KL Div: 25.0347, Entropy: 2.2969\n",
      "Epoch[6/10], Step [90/469], Reconst Loss: 82.3813, KL Div: 25.3053, Entropy: 2.2976\n",
      "Epoch[6/10], Step [100/469], Reconst Loss: 82.7475, KL Div: 24.5849, Entropy: 2.2975\n",
      "Epoch[6/10], Step [110/469], Reconst Loss: 86.8629, KL Div: 24.6281, Entropy: 2.2979\n",
      "Epoch[6/10], Step [120/469], Reconst Loss: 83.4906, KL Div: 25.9807, Entropy: 2.2976\n",
      "Epoch[6/10], Step [130/469], Reconst Loss: 80.1073, KL Div: 24.2046, Entropy: 2.2970\n",
      "Epoch[6/10], Step [140/469], Reconst Loss: 83.6490, KL Div: 25.2635, Entropy: 2.2978\n",
      "Epoch[6/10], Step [150/469], Reconst Loss: 84.4076, KL Div: 23.8549, Entropy: 2.2983\n",
      "Epoch[6/10], Step [160/469], Reconst Loss: 84.1924, KL Div: 25.1716, Entropy: 2.2973\n",
      "Epoch[6/10], Step [170/469], Reconst Loss: 87.8914, KL Div: 25.6727, Entropy: 2.2980\n",
      "Epoch[6/10], Step [180/469], Reconst Loss: 84.5040, KL Div: 25.1742, Entropy: 2.2975\n",
      "Epoch[6/10], Step [190/469], Reconst Loss: 82.8332, KL Div: 23.7703, Entropy: 2.2974\n",
      "Epoch[6/10], Step [200/469], Reconst Loss: 86.1091, KL Div: 25.4634, Entropy: 2.2965\n",
      "Epoch[6/10], Step [210/469], Reconst Loss: 83.3049, KL Div: 24.4040, Entropy: 2.2969\n",
      "Epoch[6/10], Step [220/469], Reconst Loss: 83.4204, KL Div: 25.0587, Entropy: 2.2985\n",
      "Epoch[6/10], Step [230/469], Reconst Loss: 84.1093, KL Div: 25.0074, Entropy: 2.2974\n",
      "Epoch[6/10], Step [240/469], Reconst Loss: 85.7449, KL Div: 25.2777, Entropy: 2.2976\n",
      "Epoch[6/10], Step [250/469], Reconst Loss: 80.5378, KL Div: 24.5050, Entropy: 2.2974\n",
      "Epoch[6/10], Step [260/469], Reconst Loss: 83.7226, KL Div: 25.1036, Entropy: 2.2971\n",
      "Epoch[6/10], Step [270/469], Reconst Loss: 84.0952, KL Div: 24.5556, Entropy: 2.2981\n",
      "Epoch[6/10], Step [280/469], Reconst Loss: 82.7681, KL Div: 24.2019, Entropy: 2.2985\n",
      "Epoch[6/10], Step [290/469], Reconst Loss: 80.5562, KL Div: 24.4432, Entropy: 2.2979\n",
      "Epoch[6/10], Step [300/469], Reconst Loss: 83.0749, KL Div: 25.1053, Entropy: 2.2974\n",
      "Epoch[6/10], Step [310/469], Reconst Loss: 86.6340, KL Div: 25.7869, Entropy: 2.2979\n",
      "Epoch[6/10], Step [320/469], Reconst Loss: 83.3152, KL Div: 25.0980, Entropy: 2.2974\n",
      "Epoch[6/10], Step [330/469], Reconst Loss: 83.4479, KL Div: 25.6297, Entropy: 2.2976\n",
      "Epoch[6/10], Step [340/469], Reconst Loss: 81.2110, KL Div: 24.8995, Entropy: 2.2979\n",
      "Epoch[6/10], Step [350/469], Reconst Loss: 81.6782, KL Div: 24.3781, Entropy: 2.2972\n",
      "Epoch[6/10], Step [360/469], Reconst Loss: 84.4631, KL Div: 24.8665, Entropy: 2.2980\n",
      "Epoch[6/10], Step [370/469], Reconst Loss: 80.5381, KL Div: 25.2548, Entropy: 2.2982\n",
      "Epoch[6/10], Step [380/469], Reconst Loss: 84.9017, KL Div: 25.7480, Entropy: 2.2973\n",
      "Epoch[6/10], Step [390/469], Reconst Loss: 84.3733, KL Div: 25.3672, Entropy: 2.2978\n",
      "Epoch[6/10], Step [400/469], Reconst Loss: 83.0254, KL Div: 24.7425, Entropy: 2.2978\n",
      "Epoch[6/10], Step [410/469], Reconst Loss: 86.2566, KL Div: 25.9176, Entropy: 2.2978\n",
      "Epoch[6/10], Step [420/469], Reconst Loss: 84.1628, KL Div: 25.2024, Entropy: 2.2980\n",
      "Epoch[6/10], Step [430/469], Reconst Loss: 85.2639, KL Div: 24.7210, Entropy: 2.2984\n",
      "Epoch[6/10], Step [440/469], Reconst Loss: 80.8281, KL Div: 25.3332, Entropy: 2.2984\n",
      "Epoch[6/10], Step [450/469], Reconst Loss: 83.8110, KL Div: 24.3721, Entropy: 2.2985\n",
      "Epoch[6/10], Step [460/469], Reconst Loss: 79.8566, KL Div: 24.2226, Entropy: 2.2987\n",
      "Epoch[7/10], Step [10/469], Reconst Loss: 80.4524, KL Div: 24.3129, Entropy: 2.2982\n",
      "Epoch[7/10], Step [20/469], Reconst Loss: 82.9184, KL Div: 24.8176, Entropy: 2.2976\n",
      "Epoch[7/10], Step [30/469], Reconst Loss: 83.1157, KL Div: 24.5256, Entropy: 2.2978\n",
      "Epoch[7/10], Step [40/469], Reconst Loss: 86.2074, KL Div: 25.5954, Entropy: 2.2983\n",
      "Epoch[7/10], Step [50/469], Reconst Loss: 80.8829, KL Div: 24.2108, Entropy: 2.2978\n",
      "Epoch[7/10], Step [60/469], Reconst Loss: 79.5134, KL Div: 24.4474, Entropy: 2.2985\n",
      "Epoch[7/10], Step [70/469], Reconst Loss: 82.5023, KL Div: 24.9405, Entropy: 2.2984\n",
      "Epoch[7/10], Step [80/469], Reconst Loss: 83.8093, KL Div: 25.4265, Entropy: 2.2976\n",
      "Epoch[7/10], Step [90/469], Reconst Loss: 85.2552, KL Div: 25.3056, Entropy: 2.2977\n",
      "Epoch[7/10], Step [100/469], Reconst Loss: 82.0951, KL Div: 24.0691, Entropy: 2.2983\n",
      "Epoch[7/10], Step [110/469], Reconst Loss: 84.3967, KL Div: 25.7430, Entropy: 2.2972\n",
      "Epoch[7/10], Step [120/469], Reconst Loss: 85.1446, KL Div: 24.9379, Entropy: 2.2982\n",
      "Epoch[7/10], Step [130/469], Reconst Loss: 84.5588, KL Div: 25.0186, Entropy: 2.2982\n",
      "Epoch[7/10], Step [140/469], Reconst Loss: 81.3923, KL Div: 24.0425, Entropy: 2.2982\n",
      "Epoch[7/10], Step [150/469], Reconst Loss: 85.6016, KL Div: 25.6159, Entropy: 2.2980\n",
      "Epoch[7/10], Step [160/469], Reconst Loss: 85.9698, KL Div: 26.2509, Entropy: 2.2985\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[7/10], Step [170/469], Reconst Loss: 82.7896, KL Div: 24.8975, Entropy: 2.2987\n",
      "Epoch[7/10], Step [180/469], Reconst Loss: 82.7746, KL Div: 25.7662, Entropy: 2.2988\n",
      "Epoch[7/10], Step [190/469], Reconst Loss: 79.5950, KL Div: 24.3967, Entropy: 2.2988\n",
      "Epoch[7/10], Step [200/469], Reconst Loss: 83.2642, KL Div: 25.0337, Entropy: 2.2980\n",
      "Epoch[7/10], Step [210/469], Reconst Loss: 84.4279, KL Div: 25.7871, Entropy: 2.2984\n",
      "Epoch[7/10], Step [220/469], Reconst Loss: 85.1184, KL Div: 25.2723, Entropy: 2.2985\n",
      "Epoch[7/10], Step [230/469], Reconst Loss: 87.7401, KL Div: 25.3535, Entropy: 2.2981\n",
      "Epoch[7/10], Step [240/469], Reconst Loss: 80.2863, KL Div: 23.6896, Entropy: 2.2986\n",
      "Epoch[7/10], Step [250/469], Reconst Loss: 85.0888, KL Div: 26.1101, Entropy: 2.2978\n",
      "Epoch[7/10], Step [260/469], Reconst Loss: 82.2043, KL Div: 24.3746, Entropy: 2.2984\n",
      "Epoch[7/10], Step [270/469], Reconst Loss: 79.9819, KL Div: 25.9161, Entropy: 2.2979\n",
      "Epoch[7/10], Step [280/469], Reconst Loss: 83.9070, KL Div: 25.4990, Entropy: 2.2970\n",
      "Epoch[7/10], Step [290/469], Reconst Loss: 85.3814, KL Div: 24.7239, Entropy: 2.2982\n",
      "Epoch[7/10], Step [300/469], Reconst Loss: 82.6073, KL Div: 25.3724, Entropy: 2.2984\n",
      "Epoch[7/10], Step [310/469], Reconst Loss: 85.2555, KL Div: 25.6229, Entropy: 2.2985\n",
      "Epoch[7/10], Step [320/469], Reconst Loss: 83.8401, KL Div: 25.0317, Entropy: 2.2980\n",
      "Epoch[7/10], Step [330/469], Reconst Loss: 82.1557, KL Div: 24.9074, Entropy: 2.2978\n",
      "Epoch[7/10], Step [340/469], Reconst Loss: 82.9642, KL Div: 25.2291, Entropy: 2.2974\n",
      "Epoch[7/10], Step [350/469], Reconst Loss: 79.4719, KL Div: 24.9390, Entropy: 2.2979\n",
      "Epoch[7/10], Step [360/469], Reconst Loss: 83.3187, KL Div: 24.6381, Entropy: 2.2985\n",
      "Epoch[7/10], Step [370/469], Reconst Loss: 81.3714, KL Div: 24.8356, Entropy: 2.2983\n",
      "Epoch[7/10], Step [380/469], Reconst Loss: 82.6665, KL Div: 25.0658, Entropy: 2.2983\n",
      "Epoch[7/10], Step [390/469], Reconst Loss: 81.0993, KL Div: 25.3332, Entropy: 2.2975\n",
      "Epoch[7/10], Step [400/469], Reconst Loss: 80.8290, KL Div: 25.0279, Entropy: 2.2984\n",
      "Epoch[7/10], Step [410/469], Reconst Loss: 80.3793, KL Div: 23.7076, Entropy: 2.2987\n",
      "Epoch[7/10], Step [420/469], Reconst Loss: 82.2138, KL Div: 25.4096, Entropy: 2.2988\n",
      "Epoch[7/10], Step [430/469], Reconst Loss: 86.0781, KL Div: 25.3087, Entropy: 2.2982\n",
      "Epoch[7/10], Step [440/469], Reconst Loss: 80.7477, KL Div: 25.1050, Entropy: 2.2990\n",
      "Epoch[7/10], Step [450/469], Reconst Loss: 87.3610, KL Div: 25.2574, Entropy: 2.2984\n",
      "Epoch[7/10], Step [460/469], Reconst Loss: 80.0358, KL Div: 25.5756, Entropy: 2.2982\n",
      "Epoch[8/10], Step [10/469], Reconst Loss: 83.5341, KL Div: 24.9631, Entropy: 2.2978\n",
      "Epoch[8/10], Step [20/469], Reconst Loss: 83.8193, KL Div: 25.7482, Entropy: 2.2974\n",
      "Epoch[8/10], Step [30/469], Reconst Loss: 81.0054, KL Div: 25.3131, Entropy: 2.2981\n",
      "Epoch[8/10], Step [40/469], Reconst Loss: 82.9245, KL Div: 24.0873, Entropy: 2.2979\n",
      "Epoch[8/10], Step [50/469], Reconst Loss: 82.5122, KL Div: 25.1249, Entropy: 2.2983\n",
      "Epoch[8/10], Step [60/469], Reconst Loss: 84.4332, KL Div: 24.8930, Entropy: 2.2986\n",
      "Epoch[8/10], Step [70/469], Reconst Loss: 82.6159, KL Div: 25.9170, Entropy: 2.2980\n",
      "Epoch[8/10], Step [80/469], Reconst Loss: 84.5013, KL Div: 24.5844, Entropy: 2.2984\n",
      "Epoch[8/10], Step [90/469], Reconst Loss: 77.2597, KL Div: 24.5235, Entropy: 2.2987\n",
      "Epoch[8/10], Step [100/469], Reconst Loss: 82.0911, KL Div: 25.2120, Entropy: 2.2988\n",
      "Epoch[8/10], Step [110/469], Reconst Loss: 85.3051, KL Div: 25.8846, Entropy: 2.2982\n",
      "Epoch[8/10], Step [120/469], Reconst Loss: 81.0427, KL Div: 24.7808, Entropy: 2.2985\n",
      "Epoch[8/10], Step [130/469], Reconst Loss: 81.3952, KL Div: 25.7020, Entropy: 2.2986\n",
      "Epoch[8/10], Step [140/469], Reconst Loss: 80.0447, KL Div: 24.6104, Entropy: 2.2987\n",
      "Epoch[8/10], Step [150/469], Reconst Loss: 81.7054, KL Div: 26.0440, Entropy: 2.2982\n",
      "Epoch[8/10], Step [160/469], Reconst Loss: 83.1143, KL Div: 25.0499, Entropy: 2.2985\n",
      "Epoch[8/10], Step [170/469], Reconst Loss: 81.6697, KL Div: 24.5992, Entropy: 2.2992\n",
      "Epoch[8/10], Step [180/469], Reconst Loss: 80.9781, KL Div: 24.9599, Entropy: 2.2990\n",
      "Epoch[8/10], Step [190/469], Reconst Loss: 82.3441, KL Div: 24.1407, Entropy: 2.2980\n",
      "Epoch[8/10], Step [200/469], Reconst Loss: 79.5049, KL Div: 25.4935, Entropy: 2.2983\n",
      "Epoch[8/10], Step [210/469], Reconst Loss: 82.9923, KL Div: 25.0562, Entropy: 2.2982\n",
      "Epoch[8/10], Step [220/469], Reconst Loss: 81.1137, KL Div: 25.0358, Entropy: 2.2988\n",
      "Epoch[8/10], Step [230/469], Reconst Loss: 81.0056, KL Div: 25.9608, Entropy: 2.2986\n",
      "Epoch[8/10], Step [240/469], Reconst Loss: 78.0981, KL Div: 24.5833, Entropy: 2.2988\n",
      "Epoch[8/10], Step [250/469], Reconst Loss: 83.5121, KL Div: 25.3905, Entropy: 2.2979\n",
      "Epoch[8/10], Step [260/469], Reconst Loss: 86.0481, KL Div: 25.3864, Entropy: 2.2984\n",
      "Epoch[8/10], Step [270/469], Reconst Loss: 81.3288, KL Div: 24.6920, Entropy: 2.2982\n",
      "Epoch[8/10], Step [280/469], Reconst Loss: 80.5843, KL Div: 24.8703, Entropy: 2.2989\n",
      "Epoch[8/10], Step [290/469], Reconst Loss: 79.6215, KL Div: 25.0471, Entropy: 2.2988\n",
      "Epoch[8/10], Step [300/469], Reconst Loss: 85.3999, KL Div: 25.7633, Entropy: 2.2981\n",
      "Epoch[8/10], Step [310/469], Reconst Loss: 80.7489, KL Div: 24.2718, Entropy: 2.2990\n",
      "Epoch[8/10], Step [320/469], Reconst Loss: 82.8545, KL Div: 24.7947, Entropy: 2.2988\n",
      "Epoch[8/10], Step [330/469], Reconst Loss: 79.8673, KL Div: 24.3891, Entropy: 2.2995\n",
      "Epoch[8/10], Step [340/469], Reconst Loss: 84.6873, KL Div: 25.5116, Entropy: 2.2989\n",
      "Epoch[8/10], Step [350/469], Reconst Loss: 79.5746, KL Div: 25.2169, Entropy: 2.2980\n",
      "Epoch[8/10], Step [360/469], Reconst Loss: 80.6431, KL Div: 25.0123, Entropy: 2.2982\n",
      "Epoch[8/10], Step [370/469], Reconst Loss: 85.0308, KL Div: 25.0246, Entropy: 2.2986\n",
      "Epoch[8/10], Step [380/469], Reconst Loss: 81.5745, KL Div: 24.8765, Entropy: 2.2985\n",
      "Epoch[8/10], Step [390/469], Reconst Loss: 82.2019, KL Div: 26.1105, Entropy: 2.2988\n",
      "Epoch[8/10], Step [400/469], Reconst Loss: 81.4255, KL Div: 24.2585, Entropy: 2.2995\n",
      "Epoch[8/10], Step [410/469], Reconst Loss: 85.6865, KL Div: 25.5662, Entropy: 2.2989\n",
      "Epoch[8/10], Step [420/469], Reconst Loss: 80.8834, KL Div: 24.8675, Entropy: 2.2981\n",
      "Epoch[8/10], Step [430/469], Reconst Loss: 81.1888, KL Div: 25.2649, Entropy: 2.2985\n",
      "Epoch[8/10], Step [440/469], Reconst Loss: 82.8959, KL Div: 25.0116, Entropy: 2.2991\n",
      "Epoch[8/10], Step [450/469], Reconst Loss: 82.7096, KL Div: 24.9667, Entropy: 2.2990\n",
      "Epoch[8/10], Step [460/469], Reconst Loss: 79.7930, KL Div: 24.6791, Entropy: 2.2986\n",
      "Epoch[9/10], Step [10/469], Reconst Loss: 81.7899, KL Div: 25.3144, Entropy: 2.2986\n",
      "Epoch[9/10], Step [20/469], Reconst Loss: 83.9886, KL Div: 24.6741, Entropy: 2.2988\n",
      "Epoch[9/10], Step [30/469], Reconst Loss: 82.5714, KL Div: 25.0122, Entropy: 2.2987\n",
      "Epoch[9/10], Step [40/469], Reconst Loss: 79.6539, KL Div: 25.3009, Entropy: 2.2990\n",
      "Epoch[9/10], Step [50/469], Reconst Loss: 82.9220, KL Div: 25.1494, Entropy: 2.2985\n",
      "Epoch[9/10], Step [60/469], Reconst Loss: 80.2055, KL Div: 25.0054, Entropy: 2.2984\n",
      "Epoch[9/10], Step [70/469], Reconst Loss: 84.7897, KL Div: 25.7290, Entropy: 2.2993\n",
      "Epoch[9/10], Step [80/469], Reconst Loss: 81.5588, KL Div: 24.9687, Entropy: 2.2995\n",
      "Epoch[9/10], Step [90/469], Reconst Loss: 81.1063, KL Div: 25.2711, Entropy: 2.2989\n",
      "Epoch[9/10], Step [100/469], Reconst Loss: 81.6417, KL Div: 25.3465, Entropy: 2.2982\n",
      "Epoch[9/10], Step [110/469], Reconst Loss: 77.0728, KL Div: 25.2571, Entropy: 2.2988\n",
      "Epoch[9/10], Step [120/469], Reconst Loss: 82.4849, KL Div: 25.4064, Entropy: 2.2987\n",
      "Epoch[9/10], Step [130/469], Reconst Loss: 84.2933, KL Div: 26.0174, Entropy: 2.2988\n",
      "Epoch[9/10], Step [140/469], Reconst Loss: 81.8277, KL Div: 24.8733, Entropy: 2.2994\n",
      "Epoch[9/10], Step [150/469], Reconst Loss: 82.6077, KL Div: 25.0639, Entropy: 2.2993\n",
      "Epoch[9/10], Step [160/469], Reconst Loss: 84.5171, KL Div: 25.8444, Entropy: 2.2992\n",
      "Epoch[9/10], Step [170/469], Reconst Loss: 80.7172, KL Div: 24.7721, Entropy: 2.2995\n",
      "Epoch[9/10], Step [180/469], Reconst Loss: 77.7491, KL Div: 25.1274, Entropy: 2.2997\n",
      "Epoch[9/10], Step [190/469], Reconst Loss: 86.2551, KL Div: 26.2633, Entropy: 2.2992\n",
      "Epoch[9/10], Step [200/469], Reconst Loss: 81.0734, KL Div: 25.3161, Entropy: 2.2989\n",
      "Epoch[9/10], Step [210/469], Reconst Loss: 80.3056, KL Div: 24.8720, Entropy: 2.2995\n",
      "Epoch[9/10], Step [220/469], Reconst Loss: 81.1237, KL Div: 24.9450, Entropy: 2.2990\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[9/10], Step [230/469], Reconst Loss: 81.8200, KL Div: 25.3646, Entropy: 2.2990\n",
      "Epoch[9/10], Step [240/469], Reconst Loss: 82.4868, KL Div: 25.1250, Entropy: 2.2986\n",
      "Epoch[9/10], Step [250/469], Reconst Loss: 79.9724, KL Div: 25.0442, Entropy: 2.2993\n",
      "Epoch[9/10], Step [260/469], Reconst Loss: 81.5694, KL Div: 25.4702, Entropy: 2.2988\n",
      "Epoch[9/10], Step [270/469], Reconst Loss: 81.8968, KL Div: 24.2268, Entropy: 2.2989\n",
      "Epoch[9/10], Step [280/469], Reconst Loss: 79.0696, KL Div: 25.1668, Entropy: 2.2990\n",
      "Epoch[9/10], Step [290/469], Reconst Loss: 80.3908, KL Div: 25.3231, Entropy: 2.2989\n",
      "Epoch[9/10], Step [300/469], Reconst Loss: 81.6635, KL Div: 25.2614, Entropy: 2.2989\n",
      "Epoch[9/10], Step [310/469], Reconst Loss: 83.8744, KL Div: 24.7196, Entropy: 2.2994\n",
      "Epoch[9/10], Step [320/469], Reconst Loss: 80.5875, KL Div: 24.9981, Entropy: 2.2988\n",
      "Epoch[9/10], Step [330/469], Reconst Loss: 85.2728, KL Div: 25.2346, Entropy: 2.2995\n",
      "Epoch[9/10], Step [340/469], Reconst Loss: 81.5085, KL Div: 24.9541, Entropy: 2.2993\n",
      "Epoch[9/10], Step [350/469], Reconst Loss: 77.8542, KL Div: 24.7147, Entropy: 2.2993\n",
      "Epoch[9/10], Step [360/469], Reconst Loss: 81.2418, KL Div: 25.5278, Entropy: 2.2988\n",
      "Epoch[9/10], Step [370/469], Reconst Loss: 81.3648, KL Div: 25.6648, Entropy: 2.2977\n",
      "Epoch[9/10], Step [380/469], Reconst Loss: 79.6299, KL Div: 24.7900, Entropy: 2.2990\n",
      "Epoch[9/10], Step [390/469], Reconst Loss: 83.3838, KL Div: 25.5375, Entropy: 2.2985\n",
      "Epoch[9/10], Step [400/469], Reconst Loss: 80.6415, KL Div: 24.5783, Entropy: 2.2986\n",
      "Epoch[9/10], Step [410/469], Reconst Loss: 81.4261, KL Div: 25.0163, Entropy: 2.2991\n",
      "Epoch[9/10], Step [420/469], Reconst Loss: 83.8551, KL Div: 25.3844, Entropy: 2.2985\n",
      "Epoch[9/10], Step [430/469], Reconst Loss: 82.0253, KL Div: 26.0708, Entropy: 2.2986\n",
      "Epoch[9/10], Step [440/469], Reconst Loss: 82.0701, KL Div: 24.4233, Entropy: 2.2987\n",
      "Epoch[9/10], Step [450/469], Reconst Loss: 82.2665, KL Div: 24.8297, Entropy: 2.2994\n",
      "Epoch[9/10], Step [460/469], Reconst Loss: 79.1307, KL Div: 24.6534, Entropy: 2.2992\n",
      "Epoch[10/10], Step [10/469], Reconst Loss: 81.1386, KL Div: 25.6580, Entropy: 2.2991\n",
      "Epoch[10/10], Step [20/469], Reconst Loss: 83.2475, KL Div: 24.9365, Entropy: 2.2989\n",
      "Epoch[10/10], Step [30/469], Reconst Loss: 83.8158, KL Div: 25.1492, Entropy: 2.2989\n",
      "Epoch[10/10], Step [40/469], Reconst Loss: 77.9634, KL Div: 24.8205, Entropy: 2.2988\n",
      "Epoch[10/10], Step [50/469], Reconst Loss: 78.3492, KL Div: 24.3430, Entropy: 2.2996\n",
      "Epoch[10/10], Step [60/469], Reconst Loss: 82.5206, KL Div: 24.9442, Entropy: 2.2993\n",
      "Epoch[10/10], Step [70/469], Reconst Loss: 80.5494, KL Div: 25.6691, Entropy: 2.2993\n",
      "Epoch[10/10], Step [80/469], Reconst Loss: 80.4872, KL Div: 25.2087, Entropy: 2.2991\n",
      "Epoch[10/10], Step [90/469], Reconst Loss: 80.1870, KL Div: 25.6429, Entropy: 2.2993\n",
      "Epoch[10/10], Step [100/469], Reconst Loss: 82.0365, KL Div: 24.5958, Entropy: 2.2993\n",
      "Epoch[10/10], Step [110/469], Reconst Loss: 82.8282, KL Div: 25.4252, Entropy: 2.2993\n",
      "Epoch[10/10], Step [120/469], Reconst Loss: 84.0405, KL Div: 26.0621, Entropy: 2.2991\n",
      "Epoch[10/10], Step [130/469], Reconst Loss: 80.8207, KL Div: 24.8035, Entropy: 2.2993\n",
      "Epoch[10/10], Step [140/469], Reconst Loss: 76.6456, KL Div: 24.0929, Entropy: 2.2992\n",
      "Epoch[10/10], Step [150/469], Reconst Loss: 82.1028, KL Div: 24.9213, Entropy: 2.2990\n",
      "Epoch[10/10], Step [160/469], Reconst Loss: 85.1385, KL Div: 26.0525, Entropy: 2.2988\n",
      "Epoch[10/10], Step [170/469], Reconst Loss: 79.6350, KL Div: 24.4279, Entropy: 2.2989\n",
      "Epoch[10/10], Step [180/469], Reconst Loss: 79.8936, KL Div: 24.6901, Entropy: 2.2989\n",
      "Epoch[10/10], Step [190/469], Reconst Loss: 81.6350, KL Div: 25.3008, Entropy: 2.2988\n",
      "Epoch[10/10], Step [200/469], Reconst Loss: 80.1996, KL Div: 24.8884, Entropy: 2.2984\n",
      "Epoch[10/10], Step [210/469], Reconst Loss: 82.2688, KL Div: 25.8836, Entropy: 2.2989\n",
      "Epoch[10/10], Step [220/469], Reconst Loss: 79.9036, KL Div: 25.3983, Entropy: 2.2991\n",
      "Epoch[10/10], Step [230/469], Reconst Loss: 83.3414, KL Div: 25.4506, Entropy: 2.2992\n",
      "Epoch[10/10], Step [240/469], Reconst Loss: 79.6573, KL Div: 24.9533, Entropy: 2.2996\n",
      "Epoch[10/10], Step [250/469], Reconst Loss: 83.3447, KL Div: 25.2688, Entropy: 2.2988\n",
      "Epoch[10/10], Step [260/469], Reconst Loss: 84.9254, KL Div: 26.2069, Entropy: 2.2988\n",
      "Epoch[10/10], Step [270/469], Reconst Loss: 79.8593, KL Div: 24.4908, Entropy: 2.2984\n",
      "Epoch[10/10], Step [280/469], Reconst Loss: 80.8117, KL Div: 25.6126, Entropy: 2.2991\n",
      "Epoch[10/10], Step [290/469], Reconst Loss: 76.3132, KL Div: 24.8716, Entropy: 2.2990\n",
      "Epoch[10/10], Step [300/469], Reconst Loss: 81.6459, KL Div: 25.1914, Entropy: 2.2990\n",
      "Epoch[10/10], Step [310/469], Reconst Loss: 83.2545, KL Div: 25.9443, Entropy: 2.2993\n",
      "Epoch[10/10], Step [320/469], Reconst Loss: 82.0845, KL Div: 24.5095, Entropy: 2.2995\n",
      "Epoch[10/10], Step [330/469], Reconst Loss: 78.2848, KL Div: 25.5312, Entropy: 2.2986\n",
      "Epoch[10/10], Step [340/469], Reconst Loss: 79.5851, KL Div: 24.9675, Entropy: 2.2989\n",
      "Epoch[10/10], Step [350/469], Reconst Loss: 80.2174, KL Div: 25.1049, Entropy: 2.2988\n",
      "Epoch[10/10], Step [360/469], Reconst Loss: 80.0192, KL Div: 24.9267, Entropy: 2.2996\n",
      "Epoch[10/10], Step [370/469], Reconst Loss: 84.0622, KL Div: 26.0862, Entropy: 2.2988\n",
      "Epoch[10/10], Step [380/469], Reconst Loss: 76.1099, KL Div: 24.8316, Entropy: 2.2997\n",
      "Epoch[10/10], Step [390/469], Reconst Loss: 81.9881, KL Div: 24.7449, Entropy: 2.2996\n",
      "Epoch[10/10], Step [400/469], Reconst Loss: 80.3883, KL Div: 25.5607, Entropy: 2.2993\n",
      "Epoch[10/10], Step [410/469], Reconst Loss: 78.8234, KL Div: 24.1147, Entropy: 2.2994\n",
      "Epoch[10/10], Step [420/469], Reconst Loss: 82.0114, KL Div: 25.3478, Entropy: 2.2984\n",
      "Epoch[10/10], Step [430/469], Reconst Loss: 82.2914, KL Div: 24.9444, Entropy: 2.2993\n",
      "Epoch[10/10], Step [440/469], Reconst Loss: 78.7512, KL Div: 24.6451, Entropy: 2.2984\n",
      "Epoch[10/10], Step [450/469], Reconst Loss: 82.5740, KL Div: 25.7572, Entropy: 2.2990\n",
      "Epoch[10/10], Step [460/469], Reconst Loss: 77.7838, KL Div: 24.5303, Entropy: 2.2998\n"
     ]
    }
   ],
   "source": [
    "train_G(model_G,num_epochs=10,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NyZCzWt5Mtyw"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reconstruction(model_G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9nMSU21iMty7"
   },
   "source": [
    "This was for reconstruction, but we care more about generation. For each category, we are generating 8 samples thanks to the `plot_conditional_generation()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XWb_mYgsMty9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conditional_generation(model_G, n=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PilQVNR3MtzL"
   },
   "source": [
    "It does not look like our original idea is working...\n",
    "\n",
    "What is happening is that our network is not using the categorical variable. We can track the [normalized mutual information](https://en.wikipedia.org/wiki/Mutual_information#Normalized_variants) (see [this method in scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html)) between the true labels and the labels predicted by our network (just by taking the category with maximal probability). \n",
    "\n",
    "Change your training loop to return the normalized mutual information (NMI) for each epoch. Plot the curve to check that the NMI is actually decreasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GV8yl33sMtzN"
   },
   "source": [
    "This problem is explained in [(Burgess et al., 2018)](https://arxiv.org/abs/1804.03599) and a solution is proposed in Section 5.\n",
    "\n",
    "In order to force our network to use the categorical variable, we will change the loss according to [(Dupont, 2018)](https://arxiv.org/abs/1804.00104), Section 3 Equation (7).\n",
    "\n",
    "Implement this change in the training loop and plot the new NMI curve. For $\\beta = 20, C_z=100, C_c=100$, you should see that NMI increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TxVJLo86MtzO"
   },
   "outputs": [],
   "source": [
    "model_G = VAE_Gumbel().to(device)\n",
    "optimizer = torch.optim.Adam(model_G.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1R70kiWVMtzU"
   },
   "outputs": [],
   "source": [
    "def train_G_modified_loss(model, data_loader=data_loader,num_epochs=num_epochs, beta=1. , C_z_fin=0, C_c_fin=0, verbose=True):\n",
    "    C_z = 0\n",
    "    C_c = 0\n",
    "    nmi_scores = []\n",
    "    for epoch in range(num_epochs):\n",
    "        all_labels = []\n",
    "        all_labels_est = []\n",
    "        for i, (x, labels) in enumerate(data_loader):\n",
    "            # Forward pass\n",
    "            x = x.to(device).view(-1, image_size)\n",
    "            x_reconst, mu, log_var, log_p = model(x)\n",
    "            _,labels_est = torch.max(log_p,1)\n",
    "            all_labels += list(labels.cpu().data.numpy())\n",
    "            all_labels_est += list(labels_est.cpu().data.numpy())\n",
    "\n",
    "            # Compute reconstruction loss and kl divergence\n",
    "            # For KL divergence, see Appendix B in VAE paper or http://yunjey47.tistory.com/43\n",
    "            reconst_loss = F.binary_cross_entropy(x_reconst, x, reduction='sum')\n",
    "            kl_div =  -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())\n",
    "            H_cat =  -torch.sum(torch.exp(log_p)*log_p)\n",
    "\n",
    "            # Backprop and optimize\n",
    "            loss = reconst_loss + beta*torch.abs(kl_div-C_z) + beta*torch.abs(H_cat-C_c)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            if verbose:\n",
    "                if (i+1) % 10 == 0:\n",
    "                    print (\"Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}, Entropy: {:.4f}\" \n",
    "                           .format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item(), kl_div.item(), H_cat.item()))\n",
    "        #print(all_labels_est)\n",
    "        nmi_scores.append(normalized_mutual_info_score(all_labels,all_labels_est))\n",
    "        C_z += C_z_fin/num_epochs\n",
    "        C_c += C_c_fin/num_epochs\n",
    "        \n",
    "    return nmi_scores, all_labels_est "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tPNGg4uwSEGY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/20], Step [10/469], Reconst Loss: 47062.4023, KL Div: 65.1455, Entropy: 287.5695\n",
      "Epoch[1/20], Step [20/469], Reconst Loss: 34404.3125, KL Div: 63.7360, Entropy: 245.6900\n",
      "Epoch[1/20], Step [30/469], Reconst Loss: 30626.4395, KL Div: 23.7139, Entropy: 151.7959\n",
      "Epoch[1/20], Step [40/469], Reconst Loss: 28702.3789, KL Div: 18.4156, Entropy: 6.1042\n",
      "Epoch[1/20], Step [50/469], Reconst Loss: 27545.7969, KL Div: 11.5643, Entropy: 0.5047\n",
      "Epoch[1/20], Step [60/469], Reconst Loss: 27143.7461, KL Div: 11.2269, Entropy: 0.1183\n",
      "Epoch[1/20], Step [70/469], Reconst Loss: 25626.4844, KL Div: 8.4167, Entropy: 0.1412\n",
      "Epoch[1/20], Step [80/469], Reconst Loss: 27945.7168, KL Div: 9.7553, Entropy: 0.1313\n",
      "Epoch[1/20], Step [90/469], Reconst Loss: 27732.2539, KL Div: 10.9553, Entropy: 0.0451\n",
      "Epoch[1/20], Step [100/469], Reconst Loss: 26421.6172, KL Div: 8.1390, Entropy: 0.0410\n",
      "Epoch[1/20], Step [110/469], Reconst Loss: 26486.0898, KL Div: 10.6545, Entropy: 0.1794\n",
      "Epoch[1/20], Step [120/469], Reconst Loss: 26556.3984, KL Div: 12.9882, Entropy: 0.2028\n",
      "Epoch[1/20], Step [130/469], Reconst Loss: 26276.5410, KL Div: 10.1062, Entropy: 0.1454\n",
      "Epoch[1/20], Step [140/469], Reconst Loss: 26904.0859, KL Div: 12.7707, Entropy: 0.0311\n",
      "Epoch[1/20], Step [150/469], Reconst Loss: 26152.9727, KL Div: 12.1126, Entropy: 0.1958\n",
      "Epoch[1/20], Step [160/469], Reconst Loss: 26585.8398, KL Div: 12.2861, Entropy: 0.0626\n",
      "Epoch[1/20], Step [170/469], Reconst Loss: 25846.9883, KL Div: 13.0907, Entropy: 0.0407\n",
      "Epoch[1/20], Step [180/469], Reconst Loss: 25922.6523, KL Div: 14.4283, Entropy: 0.0898\n",
      "Epoch[1/20], Step [190/469], Reconst Loss: 26698.3496, KL Div: 13.8136, Entropy: 0.0656\n",
      "Epoch[1/20], Step [200/469], Reconst Loss: 25874.2070, KL Div: 17.1242, Entropy: 0.0721\n",
      "Epoch[1/20], Step [210/469], Reconst Loss: 26065.0039, KL Div: 15.2559, Entropy: 0.0938\n",
      "Epoch[1/20], Step [220/469], Reconst Loss: 26174.5547, KL Div: 13.5978, Entropy: 0.0577\n",
      "Epoch[1/20], Step [230/469], Reconst Loss: 26363.6152, KL Div: 15.2427, Entropy: 0.0633\n",
      "Epoch[1/20], Step [240/469], Reconst Loss: 26170.1641, KL Div: 18.0998, Entropy: 0.0462\n",
      "Epoch[1/20], Step [250/469], Reconst Loss: 25729.6660, KL Div: 17.3370, Entropy: 0.2627\n",
      "Epoch[1/20], Step [260/469], Reconst Loss: 25694.9551, KL Div: 16.9782, Entropy: 0.1467\n",
      "Epoch[1/20], Step [270/469], Reconst Loss: 25054.6230, KL Div: 14.9304, Entropy: 0.1955\n",
      "Epoch[1/20], Step [280/469], Reconst Loss: 26381.6152, KL Div: 21.8883, Entropy: 0.0513\n",
      "Epoch[1/20], Step [290/469], Reconst Loss: 25630.6523, KL Div: 15.7254, Entropy: 0.1376\n",
      "Epoch[1/20], Step [300/469], Reconst Loss: 25856.5957, KL Div: 19.4713, Entropy: 0.1955\n",
      "Epoch[1/20], Step [310/469], Reconst Loss: 26337.7227, KL Div: 26.5666, Entropy: 0.0557\n",
      "Epoch[1/20], Step [320/469], Reconst Loss: 25862.7480, KL Div: 26.8680, Entropy: 0.1466\n",
      "Epoch[1/20], Step [330/469], Reconst Loss: 25146.8535, KL Div: 16.7224, Entropy: 0.1607\n",
      "Epoch[1/20], Step [340/469], Reconst Loss: 25359.5684, KL Div: 21.4924, Entropy: 0.0995\n",
      "Epoch[1/20], Step [350/469], Reconst Loss: 26081.6211, KL Div: 24.6435, Entropy: 0.1102\n",
      "Epoch[1/20], Step [360/469], Reconst Loss: 26404.1191, KL Div: 24.1026, Entropy: 0.1039\n",
      "Epoch[1/20], Step [370/469], Reconst Loss: 27692.6992, KL Div: 23.5464, Entropy: 0.0587\n",
      "Epoch[1/20], Step [380/469], Reconst Loss: 24713.5996, KL Div: 22.5461, Entropy: 0.1753\n",
      "Epoch[1/20], Step [390/469], Reconst Loss: 25669.8086, KL Div: 28.1183, Entropy: 0.1025\n",
      "Epoch[1/20], Step [400/469], Reconst Loss: 25139.4824, KL Div: 20.8222, Entropy: 0.1130\n",
      "Epoch[1/20], Step [410/469], Reconst Loss: 26395.9688, KL Div: 24.0722, Entropy: 0.0563\n",
      "Epoch[1/20], Step [420/469], Reconst Loss: 26546.0215, KL Div: 22.0886, Entropy: 0.1531\n",
      "Epoch[1/20], Step [430/469], Reconst Loss: 26061.9961, KL Div: 19.5311, Entropy: 0.0646\n",
      "Epoch[1/20], Step [440/469], Reconst Loss: 25583.5332, KL Div: 22.6123, Entropy: 0.1070\n",
      "Epoch[1/20], Step [450/469], Reconst Loss: 26044.1660, KL Div: 25.1744, Entropy: 0.1009\n",
      "Epoch[1/20], Step [460/469], Reconst Loss: 26384.9590, KL Div: 19.3482, Entropy: 0.1031\n",
      "Epoch[2/20], Step [10/469], Reconst Loss: 25456.6992, KL Div: 18.9483, Entropy: 0.1549\n",
      "Epoch[2/20], Step [20/469], Reconst Loss: 25695.6758, KL Div: 27.4650, Entropy: 0.2153\n",
      "Epoch[2/20], Step [30/469], Reconst Loss: 26411.5352, KL Div: 21.8716, Entropy: 0.0871\n",
      "Epoch[2/20], Step [40/469], Reconst Loss: 25582.0879, KL Div: 25.5466, Entropy: 0.2477\n",
      "Epoch[2/20], Step [50/469], Reconst Loss: 26203.5410, KL Div: 24.8426, Entropy: 0.2001\n",
      "Epoch[2/20], Step [60/469], Reconst Loss: 26584.3555, KL Div: 23.3142, Entropy: 0.3764\n",
      "Epoch[2/20], Step [70/469], Reconst Loss: 25139.4062, KL Div: 23.1869, Entropy: 0.3342\n",
      "Epoch[2/20], Step [80/469], Reconst Loss: 26257.9121, KL Div: 23.5102, Entropy: 0.3298\n",
      "Epoch[2/20], Step [90/469], Reconst Loss: 25381.8164, KL Div: 27.0877, Entropy: 1.1230\n",
      "Epoch[2/20], Step [100/469], Reconst Loss: 25980.6523, KL Div: 32.2794, Entropy: 1.6093\n",
      "Epoch[2/20], Step [110/469], Reconst Loss: 25156.5469, KL Div: 27.3433, Entropy: 2.6832\n",
      "Epoch[2/20], Step [120/469], Reconst Loss: 25430.7617, KL Div: 24.7023, Entropy: 6.0290\n",
      "Epoch[2/20], Step [130/469], Reconst Loss: 26752.3750, KL Div: 37.5225, Entropy: 3.5912\n",
      "Epoch[2/20], Step [140/469], Reconst Loss: 25558.4375, KL Div: 21.5135, Entropy: 5.5444\n",
      "Epoch[2/20], Step [150/469], Reconst Loss: 25740.6641, KL Div: 31.1823, Entropy: 3.4819\n",
      "Epoch[2/20], Step [160/469], Reconst Loss: 25268.5000, KL Div: 27.7418, Entropy: 5.9706\n",
      "Epoch[2/20], Step [170/469], Reconst Loss: 26649.3301, KL Div: 41.5830, Entropy: 3.8527\n",
      "Epoch[2/20], Step [180/469], Reconst Loss: 26832.7930, KL Div: 27.0103, Entropy: 4.7441\n",
      "Epoch[2/20], Step [190/469], Reconst Loss: 26317.8008, KL Div: 29.7998, Entropy: 6.1983\n",
      "Epoch[2/20], Step [200/469], Reconst Loss: 25482.5977, KL Div: 36.3872, Entropy: 3.5766\n",
      "Epoch[2/20], Step [210/469], Reconst Loss: 26647.7480, KL Div: 33.8253, Entropy: 4.9360\n",
      "Epoch[2/20], Step [220/469], Reconst Loss: 25977.1660, KL Div: 29.4848, Entropy: 5.1495\n",
      "Epoch[2/20], Step [230/469], Reconst Loss: 25679.4707, KL Div: 27.1161, Entropy: 3.8115\n",
      "Epoch[2/20], Step [240/469], Reconst Loss: 25611.0840, KL Div: 31.9997, Entropy: 4.0977\n",
      "Epoch[2/20], Step [250/469], Reconst Loss: 25934.8672, KL Div: 32.0346, Entropy: 6.2841\n",
      "Epoch[2/20], Step [260/469], Reconst Loss: 25542.8945, KL Div: 28.9635, Entropy: 4.8459\n",
      "Epoch[2/20], Step [270/469], Reconst Loss: 26012.3457, KL Div: 39.6085, Entropy: 4.5661\n",
      "Epoch[2/20], Step [280/469], Reconst Loss: 25666.5664, KL Div: 36.0566, Entropy: 3.6733\n",
      "Epoch[2/20], Step [290/469], Reconst Loss: 25108.1094, KL Div: 26.4163, Entropy: 5.2650\n",
      "Epoch[2/20], Step [300/469], Reconst Loss: 26493.2715, KL Div: 28.9042, Entropy: 5.3753\n",
      "Epoch[2/20], Step [310/469], Reconst Loss: 26183.8477, KL Div: 31.7848, Entropy: 5.1666\n",
      "Epoch[2/20], Step [320/469], Reconst Loss: 25784.8496, KL Div: 36.3464, Entropy: 3.7402\n",
      "Epoch[2/20], Step [330/469], Reconst Loss: 26021.7188, KL Div: 35.4069, Entropy: 4.6500\n",
      "Epoch[2/20], Step [340/469], Reconst Loss: 25645.9668, KL Div: 38.8856, Entropy: 3.3605\n",
      "Epoch[2/20], Step [350/469], Reconst Loss: 26171.5977, KL Div: 46.2778, Entropy: 5.2855\n",
      "Epoch[2/20], Step [360/469], Reconst Loss: 25304.6406, KL Div: 30.1596, Entropy: 4.1202\n",
      "Epoch[2/20], Step [370/469], Reconst Loss: 25622.0234, KL Div: 40.2057, Entropy: 3.8970\n",
      "Epoch[2/20], Step [380/469], Reconst Loss: 24869.6992, KL Div: 39.9977, Entropy: 6.4910\n",
      "Epoch[2/20], Step [390/469], Reconst Loss: 25806.7188, KL Div: 37.4641, Entropy: 5.1766\n",
      "Epoch[2/20], Step [400/469], Reconst Loss: 26073.8672, KL Div: 39.2588, Entropy: 5.9040\n",
      "Epoch[2/20], Step [410/469], Reconst Loss: 25715.1504, KL Div: 42.5785, Entropy: 4.3786\n",
      "Epoch[2/20], Step [420/469], Reconst Loss: 26022.8789, KL Div: 41.2606, Entropy: 4.5043\n",
      "Epoch[2/20], Step [430/469], Reconst Loss: 25588.2285, KL Div: 35.5420, Entropy: 7.2894\n",
      "Epoch[2/20], Step [440/469], Reconst Loss: 25631.4414, KL Div: 32.5577, Entropy: 4.5900\n",
      "Epoch[2/20], Step [450/469], Reconst Loss: 25727.0820, KL Div: 42.6777, Entropy: 4.2515\n",
      "Epoch[2/20], Step [460/469], Reconst Loss: 25884.4727, KL Div: 39.1093, Entropy: 4.5750\n",
      "Epoch[3/20], Step [10/469], Reconst Loss: 25589.6055, KL Div: 47.3234, Entropy: 11.9920\n",
      "Epoch[3/20], Step [20/469], Reconst Loss: 25465.0664, KL Div: 38.9803, Entropy: 7.5083\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[3/20], Step [30/469], Reconst Loss: 25558.1465, KL Div: 42.6605, Entropy: 8.5495\n",
      "Epoch[3/20], Step [40/469], Reconst Loss: 24922.3906, KL Div: 37.3882, Entropy: 8.3776\n",
      "Epoch[3/20], Step [50/469], Reconst Loss: 25046.3086, KL Div: 40.0838, Entropy: 7.4986\n",
      "Epoch[3/20], Step [60/469], Reconst Loss: 26046.6797, KL Div: 38.2925, Entropy: 9.0085\n",
      "Epoch[3/20], Step [70/469], Reconst Loss: 26070.3379, KL Div: 43.2220, Entropy: 10.0062\n",
      "Epoch[3/20], Step [80/469], Reconst Loss: 25380.8086, KL Div: 43.9602, Entropy: 10.6186\n",
      "Epoch[3/20], Step [90/469], Reconst Loss: 25066.7637, KL Div: 43.9176, Entropy: 5.9727\n",
      "Epoch[3/20], Step [100/469], Reconst Loss: 25234.1699, KL Div: 41.6216, Entropy: 10.0730\n",
      "Epoch[3/20], Step [110/469], Reconst Loss: 25475.1875, KL Div: 45.6821, Entropy: 12.6981\n",
      "Epoch[3/20], Step [120/469], Reconst Loss: 25370.1387, KL Div: 40.4411, Entropy: 9.1413\n",
      "Epoch[3/20], Step [130/469], Reconst Loss: 25426.5312, KL Div: 55.7138, Entropy: 7.7217\n",
      "Epoch[3/20], Step [140/469], Reconst Loss: 24644.8340, KL Div: 40.9567, Entropy: 8.5691\n",
      "Epoch[3/20], Step [150/469], Reconst Loss: 26437.4473, KL Div: 44.8402, Entropy: 15.4091\n",
      "Epoch[3/20], Step [160/469], Reconst Loss: 25640.3848, KL Div: 40.8158, Entropy: 9.1891\n",
      "Epoch[3/20], Step [170/469], Reconst Loss: 24919.1445, KL Div: 39.4841, Entropy: 7.3277\n",
      "Epoch[3/20], Step [180/469], Reconst Loss: 25123.7207, KL Div: 41.5794, Entropy: 10.2678\n",
      "Epoch[3/20], Step [190/469], Reconst Loss: 25594.1309, KL Div: 42.5151, Entropy: 7.5942\n",
      "Epoch[3/20], Step [200/469], Reconst Loss: 26104.9648, KL Div: 39.9349, Entropy: 9.5117\n",
      "Epoch[3/20], Step [210/469], Reconst Loss: 25223.1758, KL Div: 49.4036, Entropy: 10.7888\n",
      "Epoch[3/20], Step [220/469], Reconst Loss: 25081.3477, KL Div: 35.3556, Entropy: 8.7617\n",
      "Epoch[3/20], Step [230/469], Reconst Loss: 25073.0781, KL Div: 52.6114, Entropy: 4.4143\n",
      "Epoch[3/20], Step [240/469], Reconst Loss: 25017.6680, KL Div: 46.9118, Entropy: 13.1807\n",
      "Epoch[3/20], Step [250/469], Reconst Loss: 25307.9844, KL Div: 40.3170, Entropy: 6.3720\n",
      "Epoch[3/20], Step [260/469], Reconst Loss: 25508.2617, KL Div: 42.7614, Entropy: 7.0703\n",
      "Epoch[3/20], Step [270/469], Reconst Loss: 24553.6719, KL Div: 51.8637, Entropy: 6.0854\n",
      "Epoch[3/20], Step [280/469], Reconst Loss: 26029.4023, KL Div: 46.5645, Entropy: 11.1894\n",
      "Epoch[3/20], Step [290/469], Reconst Loss: 24621.4258, KL Div: 51.7733, Entropy: 6.9162\n",
      "Epoch[3/20], Step [300/469], Reconst Loss: 26154.5156, KL Div: 43.1064, Entropy: 11.6633\n",
      "Epoch[3/20], Step [310/469], Reconst Loss: 26476.4062, KL Div: 45.5308, Entropy: 12.8336\n",
      "Epoch[3/20], Step [320/469], Reconst Loss: 26008.2090, KL Div: 48.5641, Entropy: 8.4366\n",
      "Epoch[3/20], Step [330/469], Reconst Loss: 24888.2090, KL Div: 41.7367, Entropy: 11.7353\n",
      "Epoch[3/20], Step [340/469], Reconst Loss: 25932.2910, KL Div: 55.3679, Entropy: 8.7937\n",
      "Epoch[3/20], Step [350/469], Reconst Loss: 26037.9648, KL Div: 49.1439, Entropy: 13.8701\n",
      "Epoch[3/20], Step [360/469], Reconst Loss: 25320.5332, KL Div: 41.8480, Entropy: 6.1934\n",
      "Epoch[3/20], Step [370/469], Reconst Loss: 26045.7227, KL Div: 49.7529, Entropy: 11.7986\n",
      "Epoch[3/20], Step [380/469], Reconst Loss: 25691.9961, KL Div: 59.8817, Entropy: 6.4894\n",
      "Epoch[3/20], Step [390/469], Reconst Loss: 25508.1855, KL Div: 47.3020, Entropy: 13.4112\n",
      "Epoch[3/20], Step [400/469], Reconst Loss: 25609.8281, KL Div: 46.9033, Entropy: 7.8609\n",
      "Epoch[3/20], Step [410/469], Reconst Loss: 25733.9453, KL Div: 49.8758, Entropy: 14.0087\n",
      "Epoch[3/20], Step [420/469], Reconst Loss: 24791.5215, KL Div: 49.6908, Entropy: 8.9977\n",
      "Epoch[3/20], Step [430/469], Reconst Loss: 25134.3652, KL Div: 50.2019, Entropy: 9.9483\n",
      "Epoch[3/20], Step [440/469], Reconst Loss: 25873.5645, KL Div: 54.9354, Entropy: 7.2676\n",
      "Epoch[3/20], Step [450/469], Reconst Loss: 25375.5254, KL Div: 49.7764, Entropy: 14.2296\n",
      "Epoch[3/20], Step [460/469], Reconst Loss: 25611.8672, KL Div: 46.3009, Entropy: 13.5483\n",
      "Epoch[4/20], Step [10/469], Reconst Loss: 25080.4688, KL Div: 49.4703, Entropy: 14.4828\n",
      "Epoch[4/20], Step [20/469], Reconst Loss: 25400.3457, KL Div: 42.6041, Entropy: 15.3458\n",
      "Epoch[4/20], Step [30/469], Reconst Loss: 25360.9961, KL Div: 45.1167, Entropy: 10.6270\n",
      "Epoch[4/20], Step [40/469], Reconst Loss: 24389.1250, KL Div: 55.5261, Entropy: 17.2308\n",
      "Epoch[4/20], Step [50/469], Reconst Loss: 24728.0449, KL Div: 46.6001, Entropy: 16.0793\n",
      "Epoch[4/20], Step [60/469], Reconst Loss: 25038.0176, KL Div: 46.3247, Entropy: 15.0563\n",
      "Epoch[4/20], Step [70/469], Reconst Loss: 25170.5918, KL Div: 58.0889, Entropy: 13.1799\n",
      "Epoch[4/20], Step [80/469], Reconst Loss: 25202.3125, KL Div: 40.7381, Entropy: 15.4746\n",
      "Epoch[4/20], Step [90/469], Reconst Loss: 25163.0566, KL Div: 43.3475, Entropy: 18.0783\n",
      "Epoch[4/20], Step [100/469], Reconst Loss: 25658.8633, KL Div: 40.7014, Entropy: 16.4662\n",
      "Epoch[4/20], Step [110/469], Reconst Loss: 25129.5820, KL Div: 39.7384, Entropy: 16.8672\n",
      "Epoch[4/20], Step [120/469], Reconst Loss: 24773.6230, KL Div: 34.4049, Entropy: 14.4991\n",
      "Epoch[4/20], Step [130/469], Reconst Loss: 25862.6484, KL Div: 30.1724, Entropy: 19.1566\n",
      "Epoch[4/20], Step [140/469], Reconst Loss: 24460.8418, KL Div: 27.6266, Entropy: 18.7570\n",
      "Epoch[4/20], Step [150/469], Reconst Loss: 24598.6445, KL Div: 23.9121, Entropy: 16.7567\n",
      "Epoch[4/20], Step [160/469], Reconst Loss: 24649.1992, KL Div: 20.1164, Entropy: 15.6309\n",
      "Epoch[4/20], Step [170/469], Reconst Loss: 23927.5898, KL Div: 23.9163, Entropy: 13.0177\n",
      "Epoch[4/20], Step [180/469], Reconst Loss: 25350.2305, KL Div: 23.3930, Entropy: 17.8494\n",
      "Epoch[4/20], Step [190/469], Reconst Loss: 24925.5625, KL Div: 21.1798, Entropy: 15.6830\n",
      "Epoch[4/20], Step [200/469], Reconst Loss: 23775.7773, KL Div: 21.0707, Entropy: 14.5876\n",
      "Epoch[4/20], Step [210/469], Reconst Loss: 25010.7383, KL Div: 22.2021, Entropy: 16.2692\n",
      "Epoch[4/20], Step [220/469], Reconst Loss: 24601.7363, KL Div: 24.0739, Entropy: 12.9745\n",
      "Epoch[4/20], Step [230/469], Reconst Loss: 24154.1484, KL Div: 20.7619, Entropy: 13.2901\n",
      "Epoch[4/20], Step [240/469], Reconst Loss: 25438.4766, KL Div: 21.9623, Entropy: 16.7128\n",
      "Epoch[4/20], Step [250/469], Reconst Loss: 24522.5918, KL Div: 22.7869, Entropy: 15.2275\n",
      "Epoch[4/20], Step [260/469], Reconst Loss: 24978.1738, KL Div: 25.6689, Entropy: 15.4119\n",
      "Epoch[4/20], Step [270/469], Reconst Loss: 25338.3438, KL Div: 28.1424, Entropy: 16.6142\n",
      "Epoch[4/20], Step [280/469], Reconst Loss: 24620.2598, KL Div: 19.3007, Entropy: 16.4473\n",
      "Epoch[4/20], Step [290/469], Reconst Loss: 24273.9219, KL Div: 22.8743, Entropy: 16.3382\n",
      "Epoch[4/20], Step [300/469], Reconst Loss: 24447.7207, KL Div: 28.1198, Entropy: 14.7071\n",
      "Epoch[4/20], Step [310/469], Reconst Loss: 24279.4375, KL Div: 21.6739, Entropy: 13.0685\n",
      "Epoch[4/20], Step [320/469], Reconst Loss: 24811.8203, KL Div: 23.3406, Entropy: 16.8188\n",
      "Epoch[4/20], Step [330/469], Reconst Loss: 24917.4141, KL Div: 25.7078, Entropy: 14.3540\n",
      "Epoch[4/20], Step [340/469], Reconst Loss: 24548.4395, KL Div: 22.4772, Entropy: 11.9118\n",
      "Epoch[4/20], Step [350/469], Reconst Loss: 25619.7148, KL Div: 24.1079, Entropy: 14.3139\n",
      "Epoch[4/20], Step [360/469], Reconst Loss: 24715.6582, KL Div: 24.7340, Entropy: 18.7549\n",
      "Epoch[4/20], Step [370/469], Reconst Loss: 24179.1484, KL Div: 22.9039, Entropy: 14.6693\n",
      "Epoch[4/20], Step [380/469], Reconst Loss: 24568.5820, KL Div: 25.3583, Entropy: 15.8131\n",
      "Epoch[4/20], Step [390/469], Reconst Loss: 23830.7969, KL Div: 21.8167, Entropy: 14.5540\n",
      "Epoch[4/20], Step [400/469], Reconst Loss: 24845.8965, KL Div: 24.4459, Entropy: 11.3144\n",
      "Epoch[4/20], Step [410/469], Reconst Loss: 24869.5547, KL Div: 24.8565, Entropy: 12.1513\n",
      "Epoch[4/20], Step [420/469], Reconst Loss: 24638.3047, KL Div: 29.6047, Entropy: 17.5718\n",
      "Epoch[4/20], Step [430/469], Reconst Loss: 24243.3320, KL Div: 26.4384, Entropy: 19.3895\n",
      "Epoch[4/20], Step [440/469], Reconst Loss: 24021.2715, KL Div: 24.1970, Entropy: 14.8047\n",
      "Epoch[4/20], Step [450/469], Reconst Loss: 24837.7930, KL Div: 27.3745, Entropy: 14.6852\n",
      "Epoch[4/20], Step [460/469], Reconst Loss: 24171.1172, KL Div: 28.7085, Entropy: 19.7694\n",
      "Epoch[5/20], Step [10/469], Reconst Loss: 24318.3594, KL Div: 24.3274, Entropy: 19.3287\n",
      "Epoch[5/20], Step [20/469], Reconst Loss: 24791.8789, KL Div: 25.4702, Entropy: 18.4863\n",
      "Epoch[5/20], Step [30/469], Reconst Loss: 24556.6309, KL Div: 29.9236, Entropy: 22.8108\n",
      "Epoch[5/20], Step [40/469], Reconst Loss: 24856.1152, KL Div: 26.1677, Entropy: 22.6167\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[5/20], Step [50/469], Reconst Loss: 24507.8125, KL Div: 32.5505, Entropy: 15.3222\n",
      "Epoch[5/20], Step [60/469], Reconst Loss: 24306.5996, KL Div: 26.7179, Entropy: 22.1922\n",
      "Epoch[5/20], Step [70/469], Reconst Loss: 24655.4062, KL Div: 30.7956, Entropy: 21.8030\n",
      "Epoch[5/20], Step [80/469], Reconst Loss: 24823.4863, KL Div: 30.4711, Entropy: 15.5709\n",
      "Epoch[5/20], Step [90/469], Reconst Loss: 24761.9160, KL Div: 25.2436, Entropy: 24.0873\n",
      "Epoch[5/20], Step [100/469], Reconst Loss: 24426.0703, KL Div: 29.1458, Entropy: 23.9899\n",
      "Epoch[5/20], Step [110/469], Reconst Loss: 24566.8320, KL Div: 30.0066, Entropy: 21.5782\n",
      "Epoch[5/20], Step [120/469], Reconst Loss: 24629.3965, KL Div: 26.5185, Entropy: 17.8507\n",
      "Epoch[5/20], Step [130/469], Reconst Loss: 25453.9023, KL Div: 28.0604, Entropy: 22.1673\n",
      "Epoch[5/20], Step [140/469], Reconst Loss: 24182.6816, KL Div: 28.3578, Entropy: 24.2568\n",
      "Epoch[5/20], Step [150/469], Reconst Loss: 24875.2422, KL Div: 28.2701, Entropy: 16.2300\n",
      "Epoch[5/20], Step [160/469], Reconst Loss: 25288.2910, KL Div: 28.3891, Entropy: 21.1598\n",
      "Epoch[5/20], Step [170/469], Reconst Loss: 25395.1074, KL Div: 28.3554, Entropy: 19.8608\n",
      "Epoch[5/20], Step [180/469], Reconst Loss: 24427.1250, KL Div: 29.7069, Entropy: 15.2374\n",
      "Epoch[5/20], Step [190/469], Reconst Loss: 24391.3672, KL Div: 24.4440, Entropy: 19.1694\n",
      "Epoch[5/20], Step [200/469], Reconst Loss: 23817.1484, KL Div: 25.2925, Entropy: 18.7724\n",
      "Epoch[5/20], Step [210/469], Reconst Loss: 24331.0039, KL Div: 26.4804, Entropy: 18.9420\n",
      "Epoch[5/20], Step [220/469], Reconst Loss: 23623.1172, KL Div: 25.1836, Entropy: 19.0843\n",
      "Epoch[5/20], Step [230/469], Reconst Loss: 25211.8398, KL Div: 27.4802, Entropy: 25.5729\n",
      "Epoch[5/20], Step [240/469], Reconst Loss: 25031.9277, KL Div: 29.9231, Entropy: 25.9105\n",
      "Epoch[5/20], Step [250/469], Reconst Loss: 24125.0371, KL Div: 30.5668, Entropy: 19.0980\n",
      "Epoch[5/20], Step [260/469], Reconst Loss: 24896.3418, KL Div: 39.7134, Entropy: 19.9956\n",
      "Epoch[5/20], Step [270/469], Reconst Loss: 24409.2305, KL Div: 29.6279, Entropy: 19.7974\n",
      "Epoch[5/20], Step [280/469], Reconst Loss: 24484.9043, KL Div: 27.4455, Entropy: 21.9717\n",
      "Epoch[5/20], Step [290/469], Reconst Loss: 23486.4922, KL Div: 30.3126, Entropy: 18.2490\n",
      "Epoch[5/20], Step [300/469], Reconst Loss: 24770.2148, KL Div: 28.4376, Entropy: 21.2730\n",
      "Epoch[5/20], Step [310/469], Reconst Loss: 25027.0352, KL Div: 32.8661, Entropy: 18.0630\n",
      "Epoch[5/20], Step [320/469], Reconst Loss: 23368.5469, KL Div: 31.7555, Entropy: 16.4432\n",
      "Epoch[5/20], Step [330/469], Reconst Loss: 24133.1211, KL Div: 31.0030, Entropy: 15.2267\n",
      "Epoch[5/20], Step [340/469], Reconst Loss: 25173.8145, KL Div: 31.2343, Entropy: 25.9144\n",
      "Epoch[5/20], Step [350/469], Reconst Loss: 24169.7852, KL Div: 27.8752, Entropy: 20.8705\n",
      "Epoch[5/20], Step [360/469], Reconst Loss: 25556.9277, KL Div: 31.5746, Entropy: 19.0218\n",
      "Epoch[5/20], Step [370/469], Reconst Loss: 24135.8359, KL Div: 31.2707, Entropy: 17.1788\n",
      "Epoch[5/20], Step [380/469], Reconst Loss: 25397.7949, KL Div: 28.3681, Entropy: 22.4097\n",
      "Epoch[5/20], Step [390/469], Reconst Loss: 23707.9805, KL Div: 26.6382, Entropy: 16.7197\n",
      "Epoch[5/20], Step [400/469], Reconst Loss: 23699.2305, KL Div: 29.5189, Entropy: 19.1724\n",
      "Epoch[5/20], Step [410/469], Reconst Loss: 23710.7246, KL Div: 33.0219, Entropy: 13.9474\n",
      "Epoch[5/20], Step [420/469], Reconst Loss: 24156.5684, KL Div: 25.1619, Entropy: 24.8472\n",
      "Epoch[5/20], Step [430/469], Reconst Loss: 24521.9609, KL Div: 35.8995, Entropy: 15.6222\n",
      "Epoch[5/20], Step [440/469], Reconst Loss: 24754.7324, KL Div: 31.6015, Entropy: 29.2572\n",
      "Epoch[5/20], Step [450/469], Reconst Loss: 24112.0293, KL Div: 31.7375, Entropy: 20.0773\n",
      "Epoch[5/20], Step [460/469], Reconst Loss: 24231.4199, KL Div: 31.0864, Entropy: 18.7725\n",
      "Epoch[6/20], Step [10/469], Reconst Loss: 24246.7266, KL Div: 29.6345, Entropy: 31.5120\n",
      "Epoch[6/20], Step [20/469], Reconst Loss: 24055.8633, KL Div: 34.4056, Entropy: 26.0165\n",
      "Epoch[6/20], Step [30/469], Reconst Loss: 23925.8867, KL Div: 31.6123, Entropy: 27.7752\n",
      "Epoch[6/20], Step [40/469], Reconst Loss: 23073.2578, KL Div: 26.9677, Entropy: 24.6563\n",
      "Epoch[6/20], Step [50/469], Reconst Loss: 23767.8145, KL Div: 26.5196, Entropy: 22.7358\n",
      "Epoch[6/20], Step [60/469], Reconst Loss: 24073.1406, KL Div: 36.1992, Entropy: 30.4124\n",
      "Epoch[6/20], Step [70/469], Reconst Loss: 23311.9570, KL Div: 33.9662, Entropy: 23.2900\n",
      "Epoch[6/20], Step [80/469], Reconst Loss: 23774.6328, KL Div: 28.3646, Entropy: 27.9998\n",
      "Epoch[6/20], Step [90/469], Reconst Loss: 23705.1973, KL Div: 30.4717, Entropy: 21.2612\n",
      "Epoch[6/20], Step [100/469], Reconst Loss: 23890.9785, KL Div: 39.4189, Entropy: 24.7971\n",
      "Epoch[6/20], Step [110/469], Reconst Loss: 24043.7793, KL Div: 24.5762, Entropy: 22.3031\n",
      "Epoch[6/20], Step [120/469], Reconst Loss: 23862.6172, KL Div: 28.0932, Entropy: 21.7840\n",
      "Epoch[6/20], Step [130/469], Reconst Loss: 23323.7383, KL Div: 31.7726, Entropy: 30.4271\n",
      "Epoch[6/20], Step [140/469], Reconst Loss: 23508.3008, KL Div: 23.9721, Entropy: 27.7199\n",
      "Epoch[6/20], Step [150/469], Reconst Loss: 24319.8379, KL Div: 34.3291, Entropy: 18.4978\n",
      "Epoch[6/20], Step [160/469], Reconst Loss: 24114.1250, KL Div: 27.2502, Entropy: 29.9588\n",
      "Epoch[6/20], Step [170/469], Reconst Loss: 24377.6055, KL Div: 35.4808, Entropy: 20.3709\n",
      "Epoch[6/20], Step [180/469], Reconst Loss: 23822.3047, KL Div: 30.8202, Entropy: 22.0034\n",
      "Epoch[6/20], Step [190/469], Reconst Loss: 24265.9219, KL Div: 36.7898, Entropy: 22.9700\n",
      "Epoch[6/20], Step [200/469], Reconst Loss: 22998.0469, KL Div: 33.4690, Entropy: 29.5159\n",
      "Epoch[6/20], Step [210/469], Reconst Loss: 23373.6582, KL Div: 30.5282, Entropy: 26.5713\n",
      "Epoch[6/20], Step [220/469], Reconst Loss: 23609.1035, KL Div: 26.9945, Entropy: 29.4941\n",
      "Epoch[6/20], Step [230/469], Reconst Loss: 23341.9980, KL Div: 26.7937, Entropy: 25.5220\n",
      "Epoch[6/20], Step [240/469], Reconst Loss: 21960.7012, KL Div: 25.9272, Entropy: 24.6198\n",
      "Epoch[6/20], Step [250/469], Reconst Loss: 23420.8691, KL Div: 30.7787, Entropy: 28.0890\n",
      "Epoch[6/20], Step [260/469], Reconst Loss: 23180.1504, KL Div: 25.9073, Entropy: 27.6378\n",
      "Epoch[6/20], Step [270/469], Reconst Loss: 23450.4688, KL Div: 26.1907, Entropy: 27.1347\n",
      "Epoch[6/20], Step [280/469], Reconst Loss: 24247.0586, KL Div: 27.4261, Entropy: 23.3132\n",
      "Epoch[6/20], Step [290/469], Reconst Loss: 23930.7891, KL Div: 29.9756, Entropy: 22.6558\n",
      "Epoch[6/20], Step [300/469], Reconst Loss: 23442.6836, KL Div: 36.1212, Entropy: 27.2898\n",
      "Epoch[6/20], Step [310/469], Reconst Loss: 23436.4258, KL Div: 39.3542, Entropy: 25.3541\n",
      "Epoch[6/20], Step [320/469], Reconst Loss: 23627.3711, KL Div: 35.3791, Entropy: 20.9885\n",
      "Epoch[6/20], Step [330/469], Reconst Loss: 24105.6094, KL Div: 39.5256, Entropy: 26.1452\n",
      "Epoch[6/20], Step [340/469], Reconst Loss: 22974.4609, KL Div: 34.2806, Entropy: 23.9822\n",
      "Epoch[6/20], Step [350/469], Reconst Loss: 23691.7188, KL Div: 30.3671, Entropy: 21.8671\n",
      "Epoch[6/20], Step [360/469], Reconst Loss: 23389.6074, KL Div: 29.6246, Entropy: 19.9133\n",
      "Epoch[6/20], Step [370/469], Reconst Loss: 23391.1328, KL Div: 33.3436, Entropy: 24.2189\n",
      "Epoch[6/20], Step [380/469], Reconst Loss: 23709.6719, KL Div: 36.1880, Entropy: 22.3770\n",
      "Epoch[6/20], Step [390/469], Reconst Loss: 24618.9648, KL Div: 33.6375, Entropy: 25.3983\n",
      "Epoch[6/20], Step [400/469], Reconst Loss: 23410.9766, KL Div: 26.6071, Entropy: 25.2900\n",
      "Epoch[6/20], Step [410/469], Reconst Loss: 23988.1758, KL Div: 29.6624, Entropy: 26.6948\n",
      "Epoch[6/20], Step [420/469], Reconst Loss: 23498.7754, KL Div: 27.3368, Entropy: 26.8681\n",
      "Epoch[6/20], Step [430/469], Reconst Loss: 22840.5020, KL Div: 41.3307, Entropy: 20.3439\n",
      "Epoch[6/20], Step [440/469], Reconst Loss: 23634.7578, KL Div: 24.9245, Entropy: 23.2452\n",
      "Epoch[6/20], Step [450/469], Reconst Loss: 23689.9199, KL Div: 30.2734, Entropy: 22.1890\n",
      "Epoch[6/20], Step [460/469], Reconst Loss: 23407.5859, KL Div: 25.1987, Entropy: 25.8332\n",
      "Epoch[7/20], Step [10/469], Reconst Loss: 24181.6113, KL Div: 28.3082, Entropy: 28.9598\n",
      "Epoch[7/20], Step [20/469], Reconst Loss: 23305.3281, KL Div: 40.6978, Entropy: 32.1225\n",
      "Epoch[7/20], Step [30/469], Reconst Loss: 22993.8828, KL Div: 29.6505, Entropy: 27.2086\n",
      "Epoch[7/20], Step [40/469], Reconst Loss: 23992.2695, KL Div: 46.1319, Entropy: 30.1909\n",
      "Epoch[7/20], Step [50/469], Reconst Loss: 22776.4883, KL Div: 37.8892, Entropy: 27.5121\n",
      "Epoch[7/20], Step [60/469], Reconst Loss: 24091.9062, KL Div: 30.4131, Entropy: 28.9839\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[7/20], Step [70/469], Reconst Loss: 22437.2344, KL Div: 39.7354, Entropy: 33.3761\n",
      "Epoch[7/20], Step [80/469], Reconst Loss: 23868.8027, KL Div: 32.4755, Entropy: 28.4508\n",
      "Epoch[7/20], Step [90/469], Reconst Loss: 23189.6582, KL Div: 36.1182, Entropy: 27.8622\n",
      "Epoch[7/20], Step [100/469], Reconst Loss: 24163.3594, KL Div: 38.5417, Entropy: 27.9678\n",
      "Epoch[7/20], Step [110/469], Reconst Loss: 22824.4688, KL Div: 34.1537, Entropy: 25.1993\n",
      "Epoch[7/20], Step [120/469], Reconst Loss: 23370.6289, KL Div: 40.4594, Entropy: 24.8665\n",
      "Epoch[7/20], Step [130/469], Reconst Loss: 23247.1836, KL Div: 39.0178, Entropy: 25.2447\n",
      "Epoch[7/20], Step [140/469], Reconst Loss: 23180.4531, KL Div: 29.4850, Entropy: 26.7937\n",
      "Epoch[7/20], Step [150/469], Reconst Loss: 23394.5938, KL Div: 49.1581, Entropy: 28.0799\n",
      "Epoch[7/20], Step [160/469], Reconst Loss: 24221.3809, KL Div: 29.2796, Entropy: 33.7076\n",
      "Epoch[7/20], Step [170/469], Reconst Loss: 24168.6406, KL Div: 37.9964, Entropy: 26.3272\n",
      "Epoch[7/20], Step [180/469], Reconst Loss: 23361.3359, KL Div: 31.8562, Entropy: 26.3489\n",
      "Epoch[7/20], Step [190/469], Reconst Loss: 23648.4355, KL Div: 38.1035, Entropy: 32.2085\n",
      "Epoch[7/20], Step [200/469], Reconst Loss: 23474.2461, KL Div: 39.9523, Entropy: 30.4422\n",
      "Epoch[7/20], Step [210/469], Reconst Loss: 23634.0020, KL Div: 35.4598, Entropy: 32.6393\n",
      "Epoch[7/20], Step [220/469], Reconst Loss: 22741.6934, KL Div: 31.9633, Entropy: 33.3194\n",
      "Epoch[7/20], Step [230/469], Reconst Loss: 24173.3457, KL Div: 43.4959, Entropy: 31.2811\n",
      "Epoch[7/20], Step [240/469], Reconst Loss: 23367.7461, KL Div: 27.0183, Entropy: 38.5548\n",
      "Epoch[7/20], Step [250/469], Reconst Loss: 23099.8086, KL Div: 39.7655, Entropy: 28.9117\n",
      "Epoch[7/20], Step [260/469], Reconst Loss: 24299.3281, KL Div: 32.0985, Entropy: 28.0673\n",
      "Epoch[7/20], Step [270/469], Reconst Loss: 23392.4648, KL Div: 36.4509, Entropy: 31.2356\n",
      "Epoch[7/20], Step [280/469], Reconst Loss: 22263.3594, KL Div: 36.9995, Entropy: 26.0457\n",
      "Epoch[7/20], Step [290/469], Reconst Loss: 23807.2812, KL Div: 28.8594, Entropy: 33.8850\n",
      "Epoch[7/20], Step [300/469], Reconst Loss: 22057.3262, KL Div: 45.5859, Entropy: 26.2084\n",
      "Epoch[7/20], Step [310/469], Reconst Loss: 22725.7754, KL Div: 30.9112, Entropy: 32.9665\n",
      "Epoch[7/20], Step [320/469], Reconst Loss: 24071.3516, KL Div: 39.7851, Entropy: 28.8364\n",
      "Epoch[7/20], Step [330/469], Reconst Loss: 22620.2422, KL Div: 30.8306, Entropy: 37.4690\n",
      "Epoch[7/20], Step [340/469], Reconst Loss: 23862.6113, KL Div: 33.7736, Entropy: 33.7316\n",
      "Epoch[7/20], Step [350/469], Reconst Loss: 24068.0664, KL Div: 34.5368, Entropy: 32.3264\n",
      "Epoch[7/20], Step [360/469], Reconst Loss: 23311.7754, KL Div: 33.3091, Entropy: 28.7343\n",
      "Epoch[7/20], Step [370/469], Reconst Loss: 23378.8008, KL Div: 46.0826, Entropy: 34.3707\n",
      "Epoch[7/20], Step [380/469], Reconst Loss: 24060.5078, KL Div: 38.4247, Entropy: 38.7831\n",
      "Epoch[7/20], Step [390/469], Reconst Loss: 24335.7988, KL Div: 36.7887, Entropy: 25.0259\n",
      "Epoch[7/20], Step [400/469], Reconst Loss: 24508.5098, KL Div: 34.8644, Entropy: 25.6748\n",
      "Epoch[7/20], Step [410/469], Reconst Loss: 22451.5664, KL Div: 33.2287, Entropy: 30.6553\n",
      "Epoch[7/20], Step [420/469], Reconst Loss: 24376.4277, KL Div: 37.6343, Entropy: 31.6955\n",
      "Epoch[7/20], Step [430/469], Reconst Loss: 23338.2695, KL Div: 30.7184, Entropy: 27.1639\n",
      "Epoch[7/20], Step [440/469], Reconst Loss: 24174.3125, KL Div: 40.1661, Entropy: 30.8565\n",
      "Epoch[7/20], Step [450/469], Reconst Loss: 24139.4219, KL Div: 37.0342, Entropy: 35.0442\n",
      "Epoch[7/20], Step [460/469], Reconst Loss: 22906.4258, KL Div: 33.7902, Entropy: 30.6227\n",
      "Epoch[8/20], Step [10/469], Reconst Loss: 22894.5664, KL Div: 45.4618, Entropy: 33.4486\n",
      "Epoch[8/20], Step [20/469], Reconst Loss: 22714.0000, KL Div: 53.2372, Entropy: 28.3894\n",
      "Epoch[8/20], Step [30/469], Reconst Loss: 22491.7500, KL Div: 41.5102, Entropy: 31.3674\n",
      "Epoch[8/20], Step [40/469], Reconst Loss: 23792.8086, KL Div: 53.5140, Entropy: 30.2411\n",
      "Epoch[8/20], Step [50/469], Reconst Loss: 23625.2305, KL Div: 47.5850, Entropy: 44.0280\n",
      "Epoch[8/20], Step [60/469], Reconst Loss: 22555.4609, KL Div: 51.5315, Entropy: 26.8687\n",
      "Epoch[8/20], Step [70/469], Reconst Loss: 23435.0703, KL Div: 41.9355, Entropy: 32.0560\n",
      "Epoch[8/20], Step [80/469], Reconst Loss: 22920.5898, KL Div: 45.9470, Entropy: 33.1617\n",
      "Epoch[8/20], Step [90/469], Reconst Loss: 23672.8750, KL Div: 39.9313, Entropy: 37.1698\n",
      "Epoch[8/20], Step [100/469], Reconst Loss: 23308.6836, KL Div: 38.6444, Entropy: 35.3685\n",
      "Epoch[8/20], Step [110/469], Reconst Loss: 23086.5391, KL Div: 51.1475, Entropy: 27.8021\n",
      "Epoch[8/20], Step [120/469], Reconst Loss: 23464.2910, KL Div: 39.4642, Entropy: 27.1509\n",
      "Epoch[8/20], Step [130/469], Reconst Loss: 23180.6523, KL Div: 57.6432, Entropy: 31.7174\n",
      "Epoch[8/20], Step [140/469], Reconst Loss: 23353.6367, KL Div: 43.9681, Entropy: 32.9467\n",
      "Epoch[8/20], Step [150/469], Reconst Loss: 23472.5820, KL Div: 35.1213, Entropy: 34.5375\n",
      "Epoch[8/20], Step [160/469], Reconst Loss: 23445.1523, KL Div: 40.7452, Entropy: 34.8212\n",
      "Epoch[8/20], Step [170/469], Reconst Loss: 22844.7461, KL Div: 42.3141, Entropy: 32.1612\n",
      "Epoch[8/20], Step [180/469], Reconst Loss: 23103.8223, KL Div: 42.3799, Entropy: 27.7149\n",
      "Epoch[8/20], Step [190/469], Reconst Loss: 23411.0625, KL Div: 34.9927, Entropy: 40.4505\n",
      "Epoch[8/20], Step [200/469], Reconst Loss: 22998.3574, KL Div: 44.2453, Entropy: 38.8224\n",
      "Epoch[8/20], Step [210/469], Reconst Loss: 22603.0586, KL Div: 34.5824, Entropy: 33.4977\n",
      "Epoch[8/20], Step [220/469], Reconst Loss: 23334.8320, KL Div: 50.8456, Entropy: 25.7880\n",
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      "Epoch[8/20], Step [240/469], Reconst Loss: 23277.0938, KL Div: 51.1632, Entropy: 42.4709\n",
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      "Epoch[8/20], Step [290/469], Reconst Loss: 22986.8086, KL Div: 38.2043, Entropy: 27.7218\n",
      "Epoch[8/20], Step [300/469], Reconst Loss: 23730.2266, KL Div: 42.8231, Entropy: 33.5828\n",
      "Epoch[8/20], Step [310/469], Reconst Loss: 22500.7539, KL Div: 38.9180, Entropy: 40.2081\n",
      "Epoch[8/20], Step [320/469], Reconst Loss: 23588.4941, KL Div: 48.2577, Entropy: 31.9551\n",
      "Epoch[8/20], Step [330/469], Reconst Loss: 23381.6465, KL Div: 40.5885, Entropy: 36.1922\n",
      "Epoch[8/20], Step [340/469], Reconst Loss: 23513.1250, KL Div: 45.1231, Entropy: 36.5318\n",
      "Epoch[8/20], Step [350/469], Reconst Loss: 22716.8828, KL Div: 43.3377, Entropy: 34.7388\n",
      "Epoch[8/20], Step [360/469], Reconst Loss: 23483.2754, KL Div: 41.2610, Entropy: 27.1644\n",
      "Epoch[8/20], Step [370/469], Reconst Loss: 23687.1895, KL Div: 46.7905, Entropy: 34.1377\n",
      "Epoch[8/20], Step [380/469], Reconst Loss: 23728.9551, KL Div: 40.4034, Entropy: 34.4580\n",
      "Epoch[8/20], Step [390/469], Reconst Loss: 22766.0312, KL Div: 46.0509, Entropy: 32.0293\n",
      "Epoch[8/20], Step [400/469], Reconst Loss: 23245.1387, KL Div: 55.5110, Entropy: 42.2400\n",
      "Epoch[8/20], Step [410/469], Reconst Loss: 23873.8438, KL Div: 40.6570, Entropy: 33.8474\n",
      "Epoch[8/20], Step [420/469], Reconst Loss: 23499.5566, KL Div: 40.6822, Entropy: 28.8730\n",
      "Epoch[8/20], Step [430/469], Reconst Loss: 23914.9453, KL Div: 41.7273, Entropy: 36.4906\n",
      "Epoch[8/20], Step [440/469], Reconst Loss: 23021.7812, KL Div: 35.7963, Entropy: 34.4546\n",
      "Epoch[8/20], Step [450/469], Reconst Loss: 22484.3047, KL Div: 52.0515, Entropy: 25.7350\n",
      "Epoch[8/20], Step [460/469], Reconst Loss: 24076.1719, KL Div: 37.6636, Entropy: 41.3350\n",
      "Epoch[9/20], Step [10/469], Reconst Loss: 22645.3027, KL Div: 33.2596, Entropy: 41.7501\n",
      "Epoch[9/20], Step [20/469], Reconst Loss: 22719.4727, KL Div: 54.6979, Entropy: 41.6007\n",
      "Epoch[9/20], Step [30/469], Reconst Loss: 23823.1367, KL Div: 40.0694, Entropy: 42.7351\n",
      "Epoch[9/20], Step [40/469], Reconst Loss: 23147.4531, KL Div: 57.9538, Entropy: 31.5162\n",
      "Epoch[9/20], Step [50/469], Reconst Loss: 22907.3867, KL Div: 44.9391, Entropy: 39.3480\n",
      "Epoch[9/20], Step [60/469], Reconst Loss: 23354.4062, KL Div: 56.1526, Entropy: 33.6676\n",
      "Epoch[9/20], Step [70/469], Reconst Loss: 23939.8438, KL Div: 51.8251, Entropy: 41.9090\n",
      "Epoch[9/20], Step [80/469], Reconst Loss: 22808.3789, KL Div: 48.4015, Entropy: 45.1855\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[9/20], Step [90/469], Reconst Loss: 22855.5234, KL Div: 39.2881, Entropy: 42.5865\n",
      "Epoch[9/20], Step [100/469], Reconst Loss: 22172.6602, KL Div: 45.5232, Entropy: 36.5259\n",
      "Epoch[9/20], Step [110/469], Reconst Loss: 22712.4238, KL Div: 44.8304, Entropy: 38.5392\n",
      "Epoch[9/20], Step [120/469], Reconst Loss: 21459.6680, KL Div: 46.8168, Entropy: 40.7979\n",
      "Epoch[9/20], Step [130/469], Reconst Loss: 22956.0566, KL Div: 53.1485, Entropy: 38.8405\n",
      "Epoch[9/20], Step [140/469], Reconst Loss: 22657.5625, KL Div: 60.3485, Entropy: 34.9459\n",
      "Epoch[9/20], Step [150/469], Reconst Loss: 23337.8672, KL Div: 41.9929, Entropy: 42.1029\n",
      "Epoch[9/20], Step [160/469], Reconst Loss: 22232.5820, KL Div: 52.6241, Entropy: 43.0888\n",
      "Epoch[9/20], Step [170/469], Reconst Loss: 22776.4531, KL Div: 45.0039, Entropy: 37.1079\n",
      "Epoch[9/20], Step [180/469], Reconst Loss: 23326.0742, KL Div: 43.8464, Entropy: 44.5537\n",
      "Epoch[9/20], Step [190/469], Reconst Loss: 22933.0586, KL Div: 50.1081, Entropy: 42.8603\n",
      "Epoch[9/20], Step [200/469], Reconst Loss: 22596.6797, KL Div: 43.9126, Entropy: 36.5145\n",
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      "Epoch[9/20], Step [220/469], Reconst Loss: 22961.9922, KL Div: 45.3942, Entropy: 44.0282\n",
      "Epoch[9/20], Step [230/469], Reconst Loss: 22572.6055, KL Div: 45.7821, Entropy: 32.6982\n",
      "Epoch[9/20], Step [240/469], Reconst Loss: 22655.1816, KL Div: 51.0085, Entropy: 31.8398\n",
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      "Epoch[9/20], Step [270/469], Reconst Loss: 23081.0000, KL Div: 42.0943, Entropy: 37.8835\n",
      "Epoch[9/20], Step [280/469], Reconst Loss: 23426.1797, KL Div: 64.9275, Entropy: 43.4210\n",
      "Epoch[9/20], Step [290/469], Reconst Loss: 22726.9688, KL Div: 50.8094, Entropy: 41.1330\n",
      "Epoch[9/20], Step [300/469], Reconst Loss: 23112.8164, KL Div: 38.0207, Entropy: 40.7148\n",
      "Epoch[9/20], Step [310/469], Reconst Loss: 23081.3242, KL Div: 53.8483, Entropy: 36.8424\n",
      "Epoch[9/20], Step [320/469], Reconst Loss: 23254.4766, KL Div: 42.7866, Entropy: 39.1939\n",
      "Epoch[9/20], Step [330/469], Reconst Loss: 23372.9160, KL Div: 56.3212, Entropy: 44.6858\n",
      "Epoch[9/20], Step [340/469], Reconst Loss: 22793.6953, KL Div: 47.2070, Entropy: 42.3834\n",
      "Epoch[9/20], Step [350/469], Reconst Loss: 22373.7695, KL Div: 46.4200, Entropy: 38.4592\n",
      "Epoch[9/20], Step [360/469], Reconst Loss: 22541.9023, KL Div: 44.7518, Entropy: 29.0833\n",
      "Epoch[9/20], Step [370/469], Reconst Loss: 21933.8320, KL Div: 48.4756, Entropy: 42.2200\n",
      "Epoch[9/20], Step [380/469], Reconst Loss: 22665.7617, KL Div: 42.3250, Entropy: 38.0537\n",
      "Epoch[9/20], Step [390/469], Reconst Loss: 22351.5430, KL Div: 43.5905, Entropy: 42.7054\n",
      "Epoch[9/20], Step [400/469], Reconst Loss: 22873.3359, KL Div: 45.1840, Entropy: 41.9478\n",
      "Epoch[9/20], Step [410/469], Reconst Loss: 22487.2344, KL Div: 53.1858, Entropy: 38.3333\n",
      "Epoch[9/20], Step [420/469], Reconst Loss: 21192.3848, KL Div: 40.2263, Entropy: 43.0618\n",
      "Epoch[9/20], Step [430/469], Reconst Loss: 21735.8281, KL Div: 40.2756, Entropy: 38.8750\n",
      "Epoch[9/20], Step [440/469], Reconst Loss: 22129.9238, KL Div: 49.8513, Entropy: 45.3437\n",
      "Epoch[9/20], Step [450/469], Reconst Loss: 22348.7148, KL Div: 43.4494, Entropy: 36.1121\n",
      "Epoch[9/20], Step [460/469], Reconst Loss: 24054.3047, KL Div: 50.5479, Entropy: 42.4238\n",
      "Epoch[10/20], Step [10/469], Reconst Loss: 22565.8516, KL Div: 57.4569, Entropy: 42.2334\n",
      "Epoch[10/20], Step [20/469], Reconst Loss: 21888.2695, KL Div: 49.5193, Entropy: 46.8042\n",
      "Epoch[10/20], Step [30/469], Reconst Loss: 22364.5273, KL Div: 54.7455, Entropy: 45.9798\n",
      "Epoch[10/20], Step [40/469], Reconst Loss: 22488.8301, KL Div: 46.6541, Entropy: 44.6574\n",
      "Epoch[10/20], Step [50/469], Reconst Loss: 21618.3164, KL Div: 55.3189, Entropy: 39.5839\n",
      "Epoch[10/20], Step [60/469], Reconst Loss: 22704.3770, KL Div: 44.0778, Entropy: 50.1880\n",
      "Epoch[10/20], Step [70/469], Reconst Loss: 23220.6973, KL Div: 48.8150, Entropy: 42.9346\n",
      "Epoch[10/20], Step [80/469], Reconst Loss: 22592.1016, KL Div: 53.7673, Entropy: 37.3253\n",
      "Epoch[10/20], Step [90/469], Reconst Loss: 24001.9531, KL Div: 60.8857, Entropy: 47.1451\n",
      "Epoch[10/20], Step [100/469], Reconst Loss: 22817.8301, KL Div: 51.1492, Entropy: 56.6791\n",
      "Epoch[10/20], Step [110/469], Reconst Loss: 22096.5059, KL Div: 50.3325, Entropy: 40.3131\n",
      "Epoch[10/20], Step [120/469], Reconst Loss: 21968.4180, KL Div: 51.1804, Entropy: 45.6564\n",
      "Epoch[10/20], Step [130/469], Reconst Loss: 22866.9062, KL Div: 45.0191, Entropy: 54.7848\n",
      "Epoch[10/20], Step [140/469], Reconst Loss: 22353.9062, KL Div: 48.2127, Entropy: 39.0294\n",
      "Epoch[10/20], Step [150/469], Reconst Loss: 22629.0820, KL Div: 52.3130, Entropy: 43.6875\n",
      "Epoch[10/20], Step [160/469], Reconst Loss: 22394.7617, KL Div: 47.9049, Entropy: 47.0942\n",
      "Epoch[10/20], Step [170/469], Reconst Loss: 22370.6719, KL Div: 55.6689, Entropy: 46.3776\n",
      "Epoch[10/20], Step [180/469], Reconst Loss: 21781.9551, KL Div: 48.2288, Entropy: 41.3366\n",
      "Epoch[10/20], Step [190/469], Reconst Loss: 22759.2422, KL Div: 44.0980, Entropy: 43.8532\n",
      "Epoch[10/20], Step [200/469], Reconst Loss: 21776.4082, KL Div: 46.1484, Entropy: 44.6041\n",
      "Epoch[10/20], Step [210/469], Reconst Loss: 21475.3555, KL Div: 53.1171, Entropy: 38.7993\n",
      "Epoch[10/20], Step [220/469], Reconst Loss: 23116.6406, KL Div: 49.7723, Entropy: 44.7270\n",
      "Epoch[10/20], Step [230/469], Reconst Loss: 22693.2012, KL Div: 45.1630, Entropy: 43.2485\n",
      "Epoch[10/20], Step [240/469], Reconst Loss: 21862.2070, KL Div: 51.7117, Entropy: 47.7482\n",
      "Epoch[10/20], Step [250/469], Reconst Loss: 22890.5449, KL Div: 53.2021, Entropy: 45.6436\n",
      "Epoch[10/20], Step [260/469], Reconst Loss: 22227.8281, KL Div: 53.4598, Entropy: 37.4367\n",
      "Epoch[10/20], Step [270/469], Reconst Loss: 22840.5234, KL Div: 45.7782, Entropy: 39.2165\n",
      "Epoch[10/20], Step [280/469], Reconst Loss: 23098.9336, KL Div: 53.5399, Entropy: 41.8074\n",
      "Epoch[10/20], Step [290/469], Reconst Loss: 22113.0000, KL Div: 54.0280, Entropy: 46.5926\n",
      "Epoch[10/20], Step [300/469], Reconst Loss: 23128.6875, KL Div: 56.9707, Entropy: 43.7705\n",
      "Epoch[10/20], Step [310/469], Reconst Loss: 21981.4062, KL Div: 59.8042, Entropy: 36.1342\n",
      "Epoch[10/20], Step [320/469], Reconst Loss: 22351.5078, KL Div: 42.2476, Entropy: 46.6765\n",
      "Epoch[10/20], Step [330/469], Reconst Loss: 22673.4727, KL Div: 63.8408, Entropy: 39.7259\n",
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      "Epoch[10/20], Step [370/469], Reconst Loss: 22648.4004, KL Div: 53.4585, Entropy: 48.2511\n",
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      "Epoch[10/20], Step [390/469], Reconst Loss: 21819.2852, KL Div: 48.8298, Entropy: 39.2536\n",
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      "Epoch[10/20], Step [410/469], Reconst Loss: 22635.8223, KL Div: 45.2452, Entropy: 50.1853\n",
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      "Epoch[10/20], Step [460/469], Reconst Loss: 21933.4219, KL Div: 48.0661, Entropy: 49.4458\n",
      "Epoch[11/20], Step [10/469], Reconst Loss: 21884.8535, KL Div: 54.6333, Entropy: 47.7218\n",
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      "Epoch[11/20], Step [30/469], Reconst Loss: 22788.9766, KL Div: 59.5975, Entropy: 49.0049\n",
      "Epoch[11/20], Step [40/469], Reconst Loss: 22076.4570, KL Div: 48.1146, Entropy: 44.9865\n",
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      "Epoch[11/20], Step [60/469], Reconst Loss: 22958.8047, KL Div: 58.8025, Entropy: 58.7288\n",
      "Epoch[11/20], Step [70/469], Reconst Loss: 23113.4336, KL Div: 52.1947, Entropy: 54.3085\n",
      "Epoch[11/20], Step [80/469], Reconst Loss: 22477.9883, KL Div: 56.0996, Entropy: 47.3255\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[11/20], Step [90/469], Reconst Loss: 22826.2656, KL Div: 54.5236, Entropy: 51.9910\n",
      "Epoch[11/20], Step [100/469], Reconst Loss: 21503.0078, KL Div: 52.7682, Entropy: 47.4799\n",
      "Epoch[11/20], Step [110/469], Reconst Loss: 22496.0625, KL Div: 46.9656, Entropy: 49.2969\n",
      "Epoch[11/20], Step [120/469], Reconst Loss: 22332.4375, KL Div: 58.4682, Entropy: 32.8499\n",
      "Epoch[11/20], Step [130/469], Reconst Loss: 23223.1738, KL Div: 52.2637, Entropy: 46.4881\n",
      "Epoch[11/20], Step [140/469], Reconst Loss: 21737.6055, KL Div: 52.5093, Entropy: 54.3853\n",
      "Epoch[11/20], Step [150/469], Reconst Loss: 22278.5469, KL Div: 59.9857, Entropy: 51.9441\n",
      "Epoch[11/20], Step [160/469], Reconst Loss: 21951.6172, KL Div: 55.6622, Entropy: 54.2502\n",
      "Epoch[11/20], Step [170/469], Reconst Loss: 22932.2891, KL Div: 57.6341, Entropy: 45.4365\n",
      "Epoch[11/20], Step [180/469], Reconst Loss: 22035.4609, KL Div: 54.9561, Entropy: 48.2800\n",
      "Epoch[11/20], Step [190/469], Reconst Loss: 22672.8418, KL Div: 57.5539, Entropy: 50.3845\n",
      "Epoch[11/20], Step [200/469], Reconst Loss: 22460.6426, KL Div: 48.7532, Entropy: 51.6903\n",
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      "Epoch[11/20], Step [220/469], Reconst Loss: 22122.4688, KL Div: 49.6168, Entropy: 44.3157\n",
      "Epoch[11/20], Step [230/469], Reconst Loss: 21750.6816, KL Div: 60.6290, Entropy: 43.2969\n",
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      "Epoch[11/20], Step [270/469], Reconst Loss: 22277.4883, KL Div: 55.7915, Entropy: 53.4446\n",
      "Epoch[11/20], Step [280/469], Reconst Loss: 22325.8828, KL Div: 57.3500, Entropy: 50.5547\n",
      "Epoch[11/20], Step [290/469], Reconst Loss: 22711.9648, KL Div: 53.9377, Entropy: 43.6690\n",
      "Epoch[11/20], Step [300/469], Reconst Loss: 22926.4180, KL Div: 59.2651, Entropy: 44.7526\n",
      "Epoch[11/20], Step [310/469], Reconst Loss: 22784.3984, KL Div: 48.8778, Entropy: 52.9096\n",
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      "Epoch[11/20], Step [350/469], Reconst Loss: 22799.9316, KL Div: 55.6624, Entropy: 42.2162\n",
      "Epoch[11/20], Step [360/469], Reconst Loss: 21914.0039, KL Div: 54.6412, Entropy: 44.1906\n",
      "Epoch[11/20], Step [370/469], Reconst Loss: 22461.1953, KL Div: 56.1041, Entropy: 55.9122\n",
      "Epoch[11/20], Step [380/469], Reconst Loss: 21772.3770, KL Div: 63.2945, Entropy: 41.7886\n",
      "Epoch[11/20], Step [390/469], Reconst Loss: 22023.8652, KL Div: 48.3663, Entropy: 49.1586\n",
      "Epoch[11/20], Step [400/469], Reconst Loss: 21232.5820, KL Div: 74.3589, Entropy: 37.8827\n",
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      "Epoch[11/20], Step [440/469], Reconst Loss: 22172.5547, KL Div: 60.4554, Entropy: 44.6650\n",
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      "Epoch[11/20], Step [460/469], Reconst Loss: 21182.8867, KL Div: 54.5862, Entropy: 43.3159\n",
      "Epoch[12/20], Step [10/469], Reconst Loss: 21654.7441, KL Div: 64.0874, Entropy: 44.1633\n",
      "Epoch[12/20], Step [20/469], Reconst Loss: 21912.5840, KL Div: 78.4238, Entropy: 51.7708\n",
      "Epoch[12/20], Step [30/469], Reconst Loss: 21997.5859, KL Div: 59.2860, Entropy: 58.2398\n",
      "Epoch[12/20], Step [40/469], Reconst Loss: 21213.4707, KL Div: 52.5674, Entropy: 57.8175\n",
      "Epoch[12/20], Step [50/469], Reconst Loss: 22255.0312, KL Div: 58.1325, Entropy: 51.8593\n",
      "Epoch[12/20], Step [60/469], Reconst Loss: 22152.5391, KL Div: 65.4554, Entropy: 52.8969\n",
      "Epoch[12/20], Step [70/469], Reconst Loss: 22224.0391, KL Div: 61.5694, Entropy: 50.9282\n",
      "Epoch[12/20], Step [80/469], Reconst Loss: 21204.4785, KL Div: 70.2087, Entropy: 50.8755\n",
      "Epoch[12/20], Step [90/469], Reconst Loss: 22722.0117, KL Div: 58.2524, Entropy: 58.4733\n",
      "Epoch[12/20], Step [100/469], Reconst Loss: 21509.3242, KL Div: 61.0401, Entropy: 51.8260\n",
      "Epoch[12/20], Step [110/469], Reconst Loss: 21436.5957, KL Div: 69.7637, Entropy: 49.3617\n",
      "Epoch[12/20], Step [120/469], Reconst Loss: 21801.3555, KL Div: 56.6882, Entropy: 49.6221\n",
      "Epoch[12/20], Step [130/469], Reconst Loss: 20944.6973, KL Div: 68.0226, Entropy: 50.5684\n",
      "Epoch[12/20], Step [140/469], Reconst Loss: 22096.0938, KL Div: 58.8018, Entropy: 47.8259\n",
      "Epoch[12/20], Step [150/469], Reconst Loss: 21913.6016, KL Div: 66.2810, Entropy: 56.9741\n",
      "Epoch[12/20], Step [160/469], Reconst Loss: 22515.7148, KL Div: 53.8127, Entropy: 57.9393\n",
      "Epoch[12/20], Step [170/469], Reconst Loss: 22501.3359, KL Div: 60.5572, Entropy: 47.0020\n",
      "Epoch[12/20], Step [180/469], Reconst Loss: 21121.4336, KL Div: 64.8646, Entropy: 41.7644\n",
      "Epoch[12/20], Step [190/469], Reconst Loss: 22547.4219, KL Div: 56.2394, Entropy: 56.3311\n",
      "Epoch[12/20], Step [200/469], Reconst Loss: 22293.6465, KL Div: 66.8857, Entropy: 64.3224\n",
      "Epoch[12/20], Step [210/469], Reconst Loss: 22181.8262, KL Div: 54.6815, Entropy: 55.9271\n",
      "Epoch[12/20], Step [220/469], Reconst Loss: 22600.9648, KL Div: 58.6274, Entropy: 65.2276\n",
      "Epoch[12/20], Step [230/469], Reconst Loss: 22194.9336, KL Div: 61.9599, Entropy: 57.8283\n",
      "Epoch[12/20], Step [240/469], Reconst Loss: 22266.5312, KL Div: 56.6566, Entropy: 49.8051\n",
      "Epoch[12/20], Step [250/469], Reconst Loss: 22432.9980, KL Div: 63.2694, Entropy: 53.7412\n",
      "Epoch[12/20], Step [260/469], Reconst Loss: 21892.2129, KL Div: 60.3382, Entropy: 58.4985\n",
      "Epoch[12/20], Step [270/469], Reconst Loss: 22177.9082, KL Div: 54.5350, Entropy: 54.8770\n",
      "Epoch[12/20], Step [280/469], Reconst Loss: 22456.0391, KL Div: 66.8328, Entropy: 65.2836\n",
      "Epoch[12/20], Step [290/469], Reconst Loss: 21934.3770, KL Div: 65.8677, Entropy: 65.9082\n",
      "Epoch[12/20], Step [300/469], Reconst Loss: 22899.6094, KL Div: 65.8017, Entropy: 56.8707\n",
      "Epoch[12/20], Step [310/469], Reconst Loss: 22106.9375, KL Div: 55.4254, Entropy: 53.4300\n",
      "Epoch[12/20], Step [320/469], Reconst Loss: 21639.3594, KL Div: 67.3841, Entropy: 58.1451\n",
      "Epoch[12/20], Step [330/469], Reconst Loss: 22309.7734, KL Div: 52.2176, Entropy: 59.6158\n",
      "Epoch[12/20], Step [340/469], Reconst Loss: 22062.8262, KL Div: 62.6716, Entropy: 53.1823\n",
      "Epoch[12/20], Step [350/469], Reconst Loss: 21642.1680, KL Div: 58.2592, Entropy: 58.1639\n",
      "Epoch[12/20], Step [360/469], Reconst Loss: 21371.1250, KL Div: 57.8595, Entropy: 61.9481\n",
      "Epoch[12/20], Step [370/469], Reconst Loss: 21102.9883, KL Div: 63.3848, Entropy: 49.9317\n",
      "Epoch[12/20], Step [380/469], Reconst Loss: 21324.6719, KL Div: 63.2439, Entropy: 54.2798\n",
      "Epoch[12/20], Step [390/469], Reconst Loss: 21159.4336, KL Div: 66.1748, Entropy: 52.5001\n",
      "Epoch[12/20], Step [400/469], Reconst Loss: 22459.2461, KL Div: 56.8599, Entropy: 53.3518\n",
      "Epoch[12/20], Step [410/469], Reconst Loss: 20538.4590, KL Div: 54.2947, Entropy: 54.2580\n",
      "Epoch[12/20], Step [420/469], Reconst Loss: 22014.6914, KL Div: 61.1406, Entropy: 58.2651\n",
      "Epoch[12/20], Step [430/469], Reconst Loss: 20652.2617, KL Div: 62.7727, Entropy: 49.9861\n",
      "Epoch[12/20], Step [440/469], Reconst Loss: 19944.1953, KL Div: 60.6022, Entropy: 52.4634\n",
      "Epoch[12/20], Step [450/469], Reconst Loss: 21220.3789, KL Div: 62.0034, Entropy: 48.5371\n",
      "Epoch[12/20], Step [460/469], Reconst Loss: 21345.6445, KL Div: 60.6769, Entropy: 62.9604\n",
      "Epoch[13/20], Step [10/469], Reconst Loss: 21224.0234, KL Div: 78.6241, Entropy: 55.3547\n",
      "Epoch[13/20], Step [20/469], Reconst Loss: 21621.7988, KL Div: 66.8341, Entropy: 58.0567\n",
      "Epoch[13/20], Step [30/469], Reconst Loss: 21889.3574, KL Div: 64.5787, Entropy: 64.5665\n",
      "Epoch[13/20], Step [40/469], Reconst Loss: 22392.0801, KL Div: 62.9157, Entropy: 57.9683\n",
      "Epoch[13/20], Step [50/469], Reconst Loss: 21333.4648, KL Div: 62.9188, Entropy: 60.7610\n",
      "Epoch[13/20], Step [60/469], Reconst Loss: 20512.9902, KL Div: 65.3650, Entropy: 62.1400\n",
      "Epoch[13/20], Step [70/469], Reconst Loss: 21685.7656, KL Div: 56.3546, Entropy: 55.0772\n",
      "Epoch[13/20], Step [80/469], Reconst Loss: 21421.3242, KL Div: 71.7278, Entropy: 64.2068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[13/20], Step [90/469], Reconst Loss: 21678.6641, KL Div: 61.0934, Entropy: 64.2992\n",
      "Epoch[13/20], Step [100/469], Reconst Loss: 21680.6758, KL Div: 67.4293, Entropy: 60.7092\n",
      "Epoch[13/20], Step [110/469], Reconst Loss: 22283.2305, KL Div: 67.5320, Entropy: 59.2412\n",
      "Epoch[13/20], Step [120/469], Reconst Loss: 21687.7070, KL Div: 66.1444, Entropy: 57.8415\n",
      "Epoch[13/20], Step [130/469], Reconst Loss: 21441.0859, KL Div: 63.4034, Entropy: 59.1113\n",
      "Epoch[13/20], Step [140/469], Reconst Loss: 22057.0391, KL Div: 57.0912, Entropy: 59.3349\n",
      "Epoch[13/20], Step [150/469], Reconst Loss: 21818.8555, KL Div: 58.9953, Entropy: 66.4671\n",
      "Epoch[13/20], Step [160/469], Reconst Loss: 21937.0195, KL Div: 60.9563, Entropy: 62.5915\n",
      "Epoch[13/20], Step [170/469], Reconst Loss: 20947.4805, KL Div: 56.2699, Entropy: 65.1602\n",
      "Epoch[13/20], Step [180/469], Reconst Loss: 21607.4727, KL Div: 66.6281, Entropy: 67.3416\n",
      "Epoch[13/20], Step [190/469], Reconst Loss: 21276.0977, KL Div: 59.4508, Entropy: 50.3951\n",
      "Epoch[13/20], Step [200/469], Reconst Loss: 21479.8027, KL Div: 69.7280, Entropy: 52.9692\n",
      "Epoch[13/20], Step [210/469], Reconst Loss: 22474.7227, KL Div: 60.0832, Entropy: 59.0245\n",
      "Epoch[13/20], Step [220/469], Reconst Loss: 19726.4102, KL Div: 72.7955, Entropy: 51.0226\n",
      "Epoch[13/20], Step [230/469], Reconst Loss: 21730.2812, KL Div: 64.0159, Entropy: 54.4946\n",
      "Epoch[13/20], Step [240/469], Reconst Loss: 21326.4004, KL Div: 74.3844, Entropy: 63.9869\n",
      "Epoch[13/20], Step [250/469], Reconst Loss: 22290.0645, KL Div: 70.0608, Entropy: 60.4956\n",
      "Epoch[13/20], Step [260/469], Reconst Loss: 21369.6094, KL Div: 63.4226, Entropy: 49.2474\n",
      "Epoch[13/20], Step [270/469], Reconst Loss: 22104.3691, KL Div: 63.8987, Entropy: 54.9403\n",
      "Epoch[13/20], Step [280/469], Reconst Loss: 21870.6426, KL Div: 64.3048, Entropy: 63.5073\n",
      "Epoch[13/20], Step [290/469], Reconst Loss: 21555.3242, KL Div: 69.7434, Entropy: 50.9664\n",
      "Epoch[13/20], Step [300/469], Reconst Loss: 21738.3984, KL Div: 66.9744, Entropy: 63.3349\n",
      "Epoch[13/20], Step [310/469], Reconst Loss: 21419.9219, KL Div: 69.1705, Entropy: 62.6850\n",
      "Epoch[13/20], Step [320/469], Reconst Loss: 21067.5098, KL Div: 66.7173, Entropy: 58.9104\n",
      "Epoch[13/20], Step [330/469], Reconst Loss: 20599.4512, KL Div: 64.7356, Entropy: 58.5903\n",
      "Epoch[13/20], Step [340/469], Reconst Loss: 20813.7500, KL Div: 60.8622, Entropy: 63.1050\n",
      "Epoch[13/20], Step [350/469], Reconst Loss: 21181.5352, KL Div: 70.0789, Entropy: 56.9580\n",
      "Epoch[13/20], Step [360/469], Reconst Loss: 22272.8242, KL Div: 53.8728, Entropy: 63.4231\n",
      "Epoch[13/20], Step [370/469], Reconst Loss: 21854.1406, KL Div: 71.4626, Entropy: 63.1382\n",
      "Epoch[13/20], Step [380/469], Reconst Loss: 21946.1348, KL Div: 57.8046, Entropy: 70.0527\n",
      "Epoch[13/20], Step [390/469], Reconst Loss: 22233.6953, KL Div: 62.7991, Entropy: 61.7639\n",
      "Epoch[13/20], Step [400/469], Reconst Loss: 20875.9531, KL Div: 74.4017, Entropy: 59.2647\n",
      "Epoch[13/20], Step [410/469], Reconst Loss: 22171.4785, KL Div: 59.0832, Entropy: 67.6722\n",
      "Epoch[13/20], Step [420/469], Reconst Loss: 21206.8203, KL Div: 64.2761, Entropy: 61.2128\n",
      "Epoch[13/20], Step [430/469], Reconst Loss: 21274.1602, KL Div: 63.6441, Entropy: 52.2411\n",
      "Epoch[13/20], Step [440/469], Reconst Loss: 21496.3906, KL Div: 62.3313, Entropy: 56.2212\n",
      "Epoch[13/20], Step [450/469], Reconst Loss: 20380.9570, KL Div: 78.1103, Entropy: 52.1261\n",
      "Epoch[13/20], Step [460/469], Reconst Loss: 21760.8086, KL Div: 59.1561, Entropy: 51.9742\n",
      "Epoch[14/20], Step [10/469], Reconst Loss: 22592.4844, KL Div: 67.6999, Entropy: 74.1785\n",
      "Epoch[14/20], Step [20/469], Reconst Loss: 21181.6406, KL Div: 61.5605, Entropy: 68.2580\n",
      "Epoch[14/20], Step [30/469], Reconst Loss: 20840.5332, KL Div: 67.3816, Entropy: 60.6230\n",
      "Epoch[14/20], Step [40/469], Reconst Loss: 20831.7500, KL Div: 73.9377, Entropy: 58.3267\n",
      "Epoch[14/20], Step [50/469], Reconst Loss: 21422.8301, KL Div: 72.8793, Entropy: 70.4884\n",
      "Epoch[14/20], Step [60/469], Reconst Loss: 21448.3281, KL Div: 65.8897, Entropy: 70.2175\n",
      "Epoch[14/20], Step [70/469], Reconst Loss: 20978.9297, KL Div: 70.7166, Entropy: 57.4214\n",
      "Epoch[14/20], Step [80/469], Reconst Loss: 21407.6328, KL Div: 60.8965, Entropy: 69.2543\n",
      "Epoch[14/20], Step [90/469], Reconst Loss: 21097.0723, KL Div: 76.9185, Entropy: 72.1234\n",
      "Epoch[14/20], Step [100/469], Reconst Loss: 20430.2656, KL Div: 66.5813, Entropy: 56.3398\n",
      "Epoch[14/20], Step [110/469], Reconst Loss: 21094.5781, KL Div: 66.8334, Entropy: 61.2077\n",
      "Epoch[14/20], Step [120/469], Reconst Loss: 22076.3711, KL Div: 64.2873, Entropy: 69.1438\n",
      "Epoch[14/20], Step [130/469], Reconst Loss: 21693.1387, KL Div: 66.8181, Entropy: 72.4615\n",
      "Epoch[14/20], Step [140/469], Reconst Loss: 20412.4102, KL Div: 76.3407, Entropy: 63.4957\n",
      "Epoch[14/20], Step [150/469], Reconst Loss: 21540.7812, KL Div: 68.0443, Entropy: 61.6101\n",
      "Epoch[14/20], Step [160/469], Reconst Loss: 21553.9219, KL Div: 75.3199, Entropy: 58.6910\n",
      "Epoch[14/20], Step [170/469], Reconst Loss: 21736.3750, KL Div: 69.7238, Entropy: 73.9595\n",
      "Epoch[14/20], Step [180/469], Reconst Loss: 20980.4961, KL Div: 61.2205, Entropy: 64.8715\n",
      "Epoch[14/20], Step [190/469], Reconst Loss: 22339.4492, KL Div: 65.9799, Entropy: 70.6172\n",
      "Epoch[14/20], Step [200/469], Reconst Loss: 20242.1172, KL Div: 68.8452, Entropy: 64.1036\n",
      "Epoch[14/20], Step [210/469], Reconst Loss: 21680.8867, KL Div: 67.0887, Entropy: 53.9376\n",
      "Epoch[14/20], Step [220/469], Reconst Loss: 21122.6855, KL Div: 65.1312, Entropy: 66.8808\n",
      "Epoch[14/20], Step [230/469], Reconst Loss: 21459.6875, KL Div: 74.3584, Entropy: 65.7915\n",
      "Epoch[14/20], Step [240/469], Reconst Loss: 21169.4258, KL Div: 72.0932, Entropy: 64.1610\n",
      "Epoch[14/20], Step [250/469], Reconst Loss: 21707.4043, KL Div: 76.4744, Entropy: 61.8582\n",
      "Epoch[14/20], Step [260/469], Reconst Loss: 20973.0898, KL Div: 73.8956, Entropy: 56.0283\n",
      "Epoch[14/20], Step [270/469], Reconst Loss: 20320.5352, KL Div: 79.4473, Entropy: 63.6789\n",
      "Epoch[14/20], Step [280/469], Reconst Loss: 21513.8984, KL Div: 75.3599, Entropy: 59.5190\n",
      "Epoch[14/20], Step [290/469], Reconst Loss: 20604.9941, KL Div: 80.5930, Entropy: 51.4833\n",
      "Epoch[14/20], Step [300/469], Reconst Loss: 20655.6328, KL Div: 68.5166, Entropy: 64.3092\n",
      "Epoch[14/20], Step [310/469], Reconst Loss: 21104.7109, KL Div: 61.9361, Entropy: 56.2064\n",
      "Epoch[14/20], Step [320/469], Reconst Loss: 20316.5117, KL Div: 72.9677, Entropy: 64.9324\n",
      "Epoch[14/20], Step [330/469], Reconst Loss: 20673.8828, KL Div: 66.6758, Entropy: 71.8749\n",
      "Epoch[14/20], Step [340/469], Reconst Loss: 20981.8809, KL Div: 68.4160, Entropy: 64.5185\n",
      "Epoch[14/20], Step [350/469], Reconst Loss: 21149.3789, KL Div: 80.6923, Entropy: 61.6804\n",
      "Epoch[14/20], Step [360/469], Reconst Loss: 21404.3086, KL Div: 71.7907, Entropy: 74.1155\n",
      "Epoch[14/20], Step [370/469], Reconst Loss: 21125.4375, KL Div: 71.6697, Entropy: 67.3233\n",
      "Epoch[14/20], Step [380/469], Reconst Loss: 21359.7773, KL Div: 60.5622, Entropy: 60.9769\n",
      "Epoch[14/20], Step [390/469], Reconst Loss: 21472.2988, KL Div: 80.5437, Entropy: 66.0673\n",
      "Epoch[14/20], Step [400/469], Reconst Loss: 22254.3008, KL Div: 63.6405, Entropy: 61.3983\n",
      "Epoch[14/20], Step [410/469], Reconst Loss: 20813.2227, KL Div: 77.6678, Entropy: 65.9744\n",
      "Epoch[14/20], Step [420/469], Reconst Loss: 21720.6074, KL Div: 60.4647, Entropy: 67.4544\n",
      "Epoch[14/20], Step [430/469], Reconst Loss: 20638.1035, KL Div: 73.4338, Entropy: 65.8777\n",
      "Epoch[14/20], Step [440/469], Reconst Loss: 21488.8887, KL Div: 69.1541, Entropy: 60.7574\n",
      "Epoch[14/20], Step [450/469], Reconst Loss: 20462.0273, KL Div: 67.1407, Entropy: 68.5836\n",
      "Epoch[14/20], Step [460/469], Reconst Loss: 20700.7656, KL Div: 72.9668, Entropy: 55.1813\n",
      "Epoch[15/20], Step [10/469], Reconst Loss: 20871.1172, KL Div: 71.2676, Entropy: 59.8969\n",
      "Epoch[15/20], Step [20/469], Reconst Loss: 20656.5664, KL Div: 75.9448, Entropy: 83.2923\n",
      "Epoch[15/20], Step [30/469], Reconst Loss: 19850.8477, KL Div: 76.6349, Entropy: 67.4591\n",
      "Epoch[15/20], Step [40/469], Reconst Loss: 21035.9297, KL Div: 73.1852, Entropy: 70.6555\n",
      "Epoch[15/20], Step [50/469], Reconst Loss: 19845.3711, KL Div: 97.1389, Entropy: 61.4192\n",
      "Epoch[15/20], Step [60/469], Reconst Loss: 21063.2930, KL Div: 74.2449, Entropy: 69.6550\n",
      "Epoch[15/20], Step [70/469], Reconst Loss: 21034.5859, KL Div: 68.3628, Entropy: 76.3780\n",
      "Epoch[15/20], Step [80/469], Reconst Loss: 20693.1328, KL Div: 76.1932, Entropy: 60.3980\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[15/20], Step [90/469], Reconst Loss: 21051.1914, KL Div: 71.6783, Entropy: 73.8373\n",
      "Epoch[15/20], Step [100/469], Reconst Loss: 20109.9941, KL Div: 78.6984, Entropy: 56.8408\n",
      "Epoch[15/20], Step [110/469], Reconst Loss: 22046.3926, KL Div: 81.3206, Entropy: 73.9994\n",
      "Epoch[15/20], Step [120/469], Reconst Loss: 21200.3203, KL Div: 74.4015, Entropy: 68.8894\n",
      "Epoch[15/20], Step [130/469], Reconst Loss: 20798.3418, KL Div: 74.7530, Entropy: 65.2244\n",
      "Epoch[15/20], Step [140/469], Reconst Loss: 20606.5742, KL Div: 73.4096, Entropy: 75.4040\n",
      "Epoch[15/20], Step [150/469], Reconst Loss: 21349.3984, KL Div: 79.7492, Entropy: 64.3996\n",
      "Epoch[15/20], Step [160/469], Reconst Loss: 21766.0273, KL Div: 81.3613, Entropy: 76.4244\n",
      "Epoch[15/20], Step [170/469], Reconst Loss: 21490.2344, KL Div: 72.5105, Entropy: 66.2851\n",
      "Epoch[15/20], Step [180/469], Reconst Loss: 21250.4023, KL Div: 77.9289, Entropy: 69.9002\n",
      "Epoch[15/20], Step [190/469], Reconst Loss: 21412.9668, KL Div: 66.2045, Entropy: 70.5065\n",
      "Epoch[15/20], Step [200/469], Reconst Loss: 20401.5625, KL Div: 66.1741, Entropy: 62.6299\n",
      "Epoch[15/20], Step [210/469], Reconst Loss: 21138.7383, KL Div: 73.2210, Entropy: 74.1633\n",
      "Epoch[15/20], Step [220/469], Reconst Loss: 20947.5781, KL Div: 81.7530, Entropy: 70.2543\n",
      "Epoch[15/20], Step [230/469], Reconst Loss: 21220.1504, KL Div: 77.4196, Entropy: 66.9368\n",
      "Epoch[15/20], Step [240/469], Reconst Loss: 21734.9238, KL Div: 81.6323, Entropy: 70.2078\n",
      "Epoch[15/20], Step [250/469], Reconst Loss: 20883.7520, KL Div: 72.7272, Entropy: 76.1280\n",
      "Epoch[15/20], Step [260/469], Reconst Loss: 21052.2461, KL Div: 80.4741, Entropy: 74.3915\n",
      "Epoch[15/20], Step [270/469], Reconst Loss: 21693.0723, KL Div: 69.7695, Entropy: 72.6168\n",
      "Epoch[15/20], Step [280/469], Reconst Loss: 21323.9395, KL Div: 85.1277, Entropy: 65.7338\n",
      "Epoch[15/20], Step [290/469], Reconst Loss: 20396.0391, KL Div: 89.3774, Entropy: 65.3417\n",
      "Epoch[15/20], Step [300/469], Reconst Loss: 20955.3262, KL Div: 63.7626, Entropy: 66.9723\n",
      "Epoch[15/20], Step [310/469], Reconst Loss: 20661.2773, KL Div: 80.2606, Entropy: 68.4557\n",
      "Epoch[15/20], Step [320/469], Reconst Loss: 21116.0527, KL Div: 78.6110, Entropy: 69.3806\n",
      "Epoch[15/20], Step [330/469], Reconst Loss: 21100.2109, KL Div: 72.4860, Entropy: 72.8085\n",
      "Epoch[15/20], Step [340/469], Reconst Loss: 20899.8984, KL Div: 83.4790, Entropy: 75.9120\n",
      "Epoch[15/20], Step [350/469], Reconst Loss: 21518.2812, KL Div: 73.2253, Entropy: 69.0310\n",
      "Epoch[15/20], Step [360/469], Reconst Loss: 21978.1816, KL Div: 63.2843, Entropy: 69.2635\n",
      "Epoch[15/20], Step [370/469], Reconst Loss: 20514.8711, KL Div: 70.7570, Entropy: 76.1469\n",
      "Epoch[15/20], Step [380/469], Reconst Loss: 21226.9297, KL Div: 78.1988, Entropy: 66.6128\n",
      "Epoch[15/20], Step [390/469], Reconst Loss: 21300.9258, KL Div: 83.4132, Entropy: 74.7068\n",
      "Epoch[15/20], Step [400/469], Reconst Loss: 21667.7422, KL Div: 70.6759, Entropy: 72.5345\n",
      "Epoch[15/20], Step [410/469], Reconst Loss: 20982.7676, KL Div: 87.8325, Entropy: 69.6867\n",
      "Epoch[15/20], Step [420/469], Reconst Loss: 20774.1973, KL Div: 67.0217, Entropy: 76.5186\n",
      "Epoch[15/20], Step [430/469], Reconst Loss: 21080.7227, KL Div: 62.1978, Entropy: 72.8321\n",
      "Epoch[15/20], Step [440/469], Reconst Loss: 20802.7500, KL Div: 76.0837, Entropy: 74.6184\n",
      "Epoch[15/20], Step [450/469], Reconst Loss: 21355.7305, KL Div: 70.4486, Entropy: 67.8968\n",
      "Epoch[15/20], Step [460/469], Reconst Loss: 20190.6621, KL Div: 87.5864, Entropy: 71.7663\n",
      "Epoch[16/20], Step [10/469], Reconst Loss: 20780.9902, KL Div: 83.4417, Entropy: 64.5102\n",
      "Epoch[16/20], Step [20/469], Reconst Loss: 20302.0078, KL Div: 89.7615, Entropy: 65.2486\n",
      "Epoch[16/20], Step [30/469], Reconst Loss: 20953.7676, KL Div: 78.5044, Entropy: 75.1114\n",
      "Epoch[16/20], Step [40/469], Reconst Loss: 20300.1328, KL Div: 79.8046, Entropy: 66.8766\n",
      "Epoch[16/20], Step [50/469], Reconst Loss: 20284.0996, KL Div: 80.7335, Entropy: 78.0494\n",
      "Epoch[16/20], Step [60/469], Reconst Loss: 22076.7109, KL Div: 78.5667, Entropy: 72.5117\n",
      "Epoch[16/20], Step [70/469], Reconst Loss: 20903.3340, KL Div: 96.3097, Entropy: 73.1010\n",
      "Epoch[16/20], Step [80/469], Reconst Loss: 20146.0859, KL Div: 78.0138, Entropy: 71.4367\n",
      "Epoch[16/20], Step [90/469], Reconst Loss: 19930.4961, KL Div: 89.7412, Entropy: 66.0630\n",
      "Epoch[16/20], Step [100/469], Reconst Loss: 21474.2188, KL Div: 73.4445, Entropy: 79.1478\n",
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      "Epoch[16/20], Step [120/469], Reconst Loss: 20283.0352, KL Div: 80.6467, Entropy: 74.6386\n",
      "Epoch[16/20], Step [130/469], Reconst Loss: 21574.6738, KL Div: 78.7122, Entropy: 74.4272\n",
      "Epoch[16/20], Step [140/469], Reconst Loss: 20263.1719, KL Div: 92.8193, Entropy: 71.6164\n",
      "Epoch[16/20], Step [150/469], Reconst Loss: 21068.7773, KL Div: 70.1119, Entropy: 74.4613\n",
      "Epoch[16/20], Step [160/469], Reconst Loss: 20005.1230, KL Div: 95.3962, Entropy: 81.3710\n",
      "Epoch[16/20], Step [170/469], Reconst Loss: 20926.2891, KL Div: 72.1514, Entropy: 78.0833\n",
      "Epoch[16/20], Step [180/469], Reconst Loss: 20243.9766, KL Div: 83.8897, Entropy: 67.9634\n",
      "Epoch[16/20], Step [190/469], Reconst Loss: 21246.9590, KL Div: 64.5309, Entropy: 85.2808\n",
      "Epoch[16/20], Step [200/469], Reconst Loss: 20227.3438, KL Div: 90.2133, Entropy: 65.3202\n",
      "Epoch[16/20], Step [210/469], Reconst Loss: 21258.8008, KL Div: 75.8099, Entropy: 75.0370\n",
      "Epoch[16/20], Step [220/469], Reconst Loss: 21975.1992, KL Div: 78.3565, Entropy: 79.5915\n",
      "Epoch[16/20], Step [230/469], Reconst Loss: 19953.2480, KL Div: 88.5729, Entropy: 72.5763\n",
      "Epoch[16/20], Step [240/469], Reconst Loss: 20562.7344, KL Div: 82.3201, Entropy: 60.7778\n",
      "Epoch[16/20], Step [250/469], Reconst Loss: 20194.6602, KL Div: 81.9921, Entropy: 71.1095\n",
      "Epoch[16/20], Step [260/469], Reconst Loss: 21451.2832, KL Div: 86.3832, Entropy: 79.5419\n",
      "Epoch[16/20], Step [270/469], Reconst Loss: 21258.9805, KL Div: 85.4239, Entropy: 83.9281\n",
      "Epoch[16/20], Step [280/469], Reconst Loss: 20372.3828, KL Div: 77.5982, Entropy: 70.2915\n",
      "Epoch[16/20], Step [290/469], Reconst Loss: 21503.9727, KL Div: 93.0558, Entropy: 81.3020\n",
      "Epoch[16/20], Step [300/469], Reconst Loss: 20941.3418, KL Div: 74.6923, Entropy: 71.1446\n",
      "Epoch[16/20], Step [310/469], Reconst Loss: 20436.5430, KL Div: 80.8703, Entropy: 81.5860\n",
      "Epoch[16/20], Step [320/469], Reconst Loss: 19902.4473, KL Div: 97.5783, Entropy: 58.3703\n",
      "Epoch[16/20], Step [330/469], Reconst Loss: 20610.3848, KL Div: 74.1642, Entropy: 73.5296\n",
      "Epoch[16/20], Step [340/469], Reconst Loss: 22687.8750, KL Div: 77.9322, Entropy: 80.8227\n",
      "Epoch[16/20], Step [350/469], Reconst Loss: 20952.2500, KL Div: 77.3231, Entropy: 77.4813\n",
      "Epoch[16/20], Step [360/469], Reconst Loss: 20743.9688, KL Div: 92.2534, Entropy: 77.4532\n",
      "Epoch[16/20], Step [370/469], Reconst Loss: 20978.4395, KL Div: 70.6259, Entropy: 73.3278\n",
      "Epoch[16/20], Step [380/469], Reconst Loss: 19943.7734, KL Div: 86.3370, Entropy: 65.4082\n",
      "Epoch[16/20], Step [390/469], Reconst Loss: 20699.9219, KL Div: 74.5348, Entropy: 68.2525\n",
      "Epoch[16/20], Step [400/469], Reconst Loss: 20780.0664, KL Div: 100.4066, Entropy: 70.8442\n",
      "Epoch[16/20], Step [410/469], Reconst Loss: 20738.0625, KL Div: 83.8164, Entropy: 75.4144\n",
      "Epoch[16/20], Step [420/469], Reconst Loss: 20915.9062, KL Div: 85.0201, Entropy: 76.1059\n",
      "Epoch[16/20], Step [430/469], Reconst Loss: 21611.2109, KL Div: 88.2103, Entropy: 75.6539\n",
      "Epoch[16/20], Step [440/469], Reconst Loss: 20250.9551, KL Div: 80.3296, Entropy: 72.7612\n",
      "Epoch[16/20], Step [450/469], Reconst Loss: 20946.3438, KL Div: 76.1195, Entropy: 73.9659\n",
      "Epoch[16/20], Step [460/469], Reconst Loss: 20175.3086, KL Div: 81.9547, Entropy: 68.9431\n",
      "Epoch[17/20], Step [10/469], Reconst Loss: 20276.5469, KL Div: 103.1263, Entropy: 73.5875\n",
      "Epoch[17/20], Step [20/469], Reconst Loss: 20277.8008, KL Div: 87.5965, Entropy: 75.7198\n",
      "Epoch[17/20], Step [30/469], Reconst Loss: 20914.4062, KL Div: 94.4889, Entropy: 73.5432\n",
      "Epoch[17/20], Step [40/469], Reconst Loss: 20108.8301, KL Div: 91.5692, Entropy: 81.7188\n",
      "Epoch[17/20], Step [50/469], Reconst Loss: 20718.3789, KL Div: 94.9381, Entropy: 69.5114\n",
      "Epoch[17/20], Step [60/469], Reconst Loss: 20837.5957, KL Div: 83.0871, Entropy: 84.2866\n",
      "Epoch[17/20], Step [70/469], Reconst Loss: 20558.7695, KL Div: 79.1111, Entropy: 82.4745\n",
      "Epoch[17/20], Step [80/469], Reconst Loss: 20569.7070, KL Div: 93.0052, Entropy: 87.1616\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[17/20], Step [90/469], Reconst Loss: 20021.7969, KL Div: 77.5831, Entropy: 72.8683\n",
      "Epoch[17/20], Step [100/469], Reconst Loss: 20587.3105, KL Div: 83.7318, Entropy: 84.8960\n",
      "Epoch[17/20], Step [110/469], Reconst Loss: 22046.9980, KL Div: 76.6324, Entropy: 75.7034\n",
      "Epoch[17/20], Step [120/469], Reconst Loss: 20856.6484, KL Div: 78.0681, Entropy: 83.8609\n",
      "Epoch[17/20], Step [130/469], Reconst Loss: 20805.1211, KL Div: 85.4768, Entropy: 77.5939\n",
      "Epoch[17/20], Step [140/469], Reconst Loss: 19738.7070, KL Div: 81.2385, Entropy: 79.6644\n",
      "Epoch[17/20], Step [150/469], Reconst Loss: 20561.7617, KL Div: 86.6955, Entropy: 79.7185\n",
      "Epoch[17/20], Step [160/469], Reconst Loss: 21119.9375, KL Div: 89.4296, Entropy: 76.9963\n",
      "Epoch[17/20], Step [170/469], Reconst Loss: 20592.9648, KL Div: 88.5571, Entropy: 79.8304\n",
      "Epoch[17/20], Step [180/469], Reconst Loss: 21272.9297, KL Div: 79.6065, Entropy: 72.6963\n",
      "Epoch[17/20], Step [190/469], Reconst Loss: 20863.5625, KL Div: 86.2599, Entropy: 76.5083\n",
      "Epoch[17/20], Step [200/469], Reconst Loss: 20297.2930, KL Div: 86.9002, Entropy: 77.1200\n",
      "Epoch[17/20], Step [210/469], Reconst Loss: 19965.4375, KL Div: 90.9617, Entropy: 75.8440\n",
      "Epoch[17/20], Step [220/469], Reconst Loss: 20306.6641, KL Div: 83.8399, Entropy: 74.9226\n",
      "Epoch[17/20], Step [230/469], Reconst Loss: 21518.0469, KL Div: 73.9184, Entropy: 87.0289\n",
      "Epoch[17/20], Step [240/469], Reconst Loss: 20414.3047, KL Div: 78.1981, Entropy: 85.4922\n",
      "Epoch[17/20], Step [250/469], Reconst Loss: 20091.6230, KL Div: 77.7809, Entropy: 66.1624\n",
      "Epoch[17/20], Step [260/469], Reconst Loss: 21149.5664, KL Div: 92.7820, Entropy: 78.4772\n",
      "Epoch[17/20], Step [270/469], Reconst Loss: 21300.2422, KL Div: 81.2933, Entropy: 83.4981\n",
      "Epoch[17/20], Step [280/469], Reconst Loss: 19155.3848, KL Div: 93.3812, Entropy: 75.1175\n",
      "Epoch[17/20], Step [290/469], Reconst Loss: 21626.3789, KL Div: 85.0761, Entropy: 86.6090\n",
      "Epoch[17/20], Step [300/469], Reconst Loss: 21561.8125, KL Div: 79.2749, Entropy: 87.1492\n",
      "Epoch[17/20], Step [310/469], Reconst Loss: 20689.3984, KL Div: 95.9689, Entropy: 75.1866\n",
      "Epoch[17/20], Step [320/469], Reconst Loss: 20542.9766, KL Div: 81.7049, Entropy: 78.9150\n",
      "Epoch[17/20], Step [330/469], Reconst Loss: 19592.9219, KL Div: 94.7524, Entropy: 72.2077\n",
      "Epoch[17/20], Step [340/469], Reconst Loss: 19793.9082, KL Div: 83.3670, Entropy: 75.0370\n",
      "Epoch[17/20], Step [350/469], Reconst Loss: 20613.0820, KL Div: 80.7991, Entropy: 89.0815\n",
      "Epoch[17/20], Step [360/469], Reconst Loss: 21012.4492, KL Div: 96.1014, Entropy: 70.8913\n",
      "Epoch[17/20], Step [370/469], Reconst Loss: 20658.3320, KL Div: 89.2556, Entropy: 76.8066\n",
      "Epoch[17/20], Step [380/469], Reconst Loss: 20188.5312, KL Div: 77.5727, Entropy: 74.1174\n",
      "Epoch[17/20], Step [390/469], Reconst Loss: 20411.9316, KL Div: 82.5147, Entropy: 74.4981\n",
      "Epoch[17/20], Step [400/469], Reconst Loss: 21160.5820, KL Div: 86.9886, Entropy: 85.4413\n",
      "Epoch[17/20], Step [410/469], Reconst Loss: 20764.1172, KL Div: 87.5833, Entropy: 72.1609\n",
      "Epoch[17/20], Step [420/469], Reconst Loss: 21214.6895, KL Div: 74.7236, Entropy: 74.7683\n",
      "Epoch[17/20], Step [430/469], Reconst Loss: 20684.2246, KL Div: 91.2790, Entropy: 84.7018\n",
      "Epoch[17/20], Step [440/469], Reconst Loss: 21312.4590, KL Div: 86.8548, Entropy: 78.3628\n",
      "Epoch[17/20], Step [450/469], Reconst Loss: 20862.7637, KL Div: 88.4331, Entropy: 77.0669\n",
      "Epoch[17/20], Step [460/469], Reconst Loss: 20993.1191, KL Div: 85.3788, Entropy: 78.4971\n",
      "Epoch[18/20], Step [10/469], Reconst Loss: 21117.5645, KL Div: 76.5993, Entropy: 75.7073\n",
      "Epoch[18/20], Step [20/469], Reconst Loss: 20892.0156, KL Div: 94.2028, Entropy: 81.7134\n",
      "Epoch[18/20], Step [30/469], Reconst Loss: 20785.0742, KL Div: 92.1395, Entropy: 91.0676\n",
      "Epoch[18/20], Step [40/469], Reconst Loss: 20505.5020, KL Div: 93.8255, Entropy: 83.9564\n",
      "Epoch[18/20], Step [50/469], Reconst Loss: 20194.9453, KL Div: 84.6045, Entropy: 76.7765\n",
      "Epoch[18/20], Step [60/469], Reconst Loss: 21721.4355, KL Div: 91.5374, Entropy: 94.9821\n",
      "Epoch[18/20], Step [70/469], Reconst Loss: 19854.1816, KL Div: 88.8536, Entropy: 81.4215\n",
      "Epoch[18/20], Step [80/469], Reconst Loss: 20566.5312, KL Div: 96.4604, Entropy: 83.6691\n",
      "Epoch[18/20], Step [90/469], Reconst Loss: 20150.8848, KL Div: 93.3099, Entropy: 75.3575\n",
      "Epoch[18/20], Step [100/469], Reconst Loss: 21366.3535, KL Div: 84.9021, Entropy: 84.0729\n",
      "Epoch[18/20], Step [110/469], Reconst Loss: 20682.6855, KL Div: 89.1421, Entropy: 90.9543\n",
      "Epoch[18/20], Step [120/469], Reconst Loss: 20898.7422, KL Div: 85.8569, Entropy: 80.3626\n",
      "Epoch[18/20], Step [130/469], Reconst Loss: 20825.6250, KL Div: 82.7618, Entropy: 77.0024\n",
      "Epoch[18/20], Step [140/469], Reconst Loss: 20480.1406, KL Div: 90.8943, Entropy: 88.9409\n",
      "Epoch[18/20], Step [150/469], Reconst Loss: 20306.4219, KL Div: 88.3107, Entropy: 71.5832\n",
      "Epoch[18/20], Step [160/469], Reconst Loss: 20043.6250, KL Div: 80.4122, Entropy: 80.6343\n",
      "Epoch[18/20], Step [170/469], Reconst Loss: 20757.9277, KL Div: 108.6287, Entropy: 87.1837\n",
      "Epoch[18/20], Step [180/469], Reconst Loss: 21499.3945, KL Div: 86.3814, Entropy: 84.3368\n",
      "Epoch[18/20], Step [190/469], Reconst Loss: 20419.7148, KL Div: 98.7749, Entropy: 79.0745\n",
      "Epoch[18/20], Step [200/469], Reconst Loss: 21270.8906, KL Div: 84.6713, Entropy: 81.9329\n",
      "Epoch[18/20], Step [210/469], Reconst Loss: 20323.7695, KL Div: 91.4526, Entropy: 78.0705\n",
      "Epoch[18/20], Step [220/469], Reconst Loss: 20518.1172, KL Div: 87.9583, Entropy: 85.3726\n",
      "Epoch[18/20], Step [230/469], Reconst Loss: 21108.3945, KL Div: 107.5384, Entropy: 80.6073\n",
      "Epoch[18/20], Step [240/469], Reconst Loss: 21773.1641, KL Div: 94.1764, Entropy: 91.4971\n",
      "Epoch[18/20], Step [250/469], Reconst Loss: 19998.0781, KL Div: 90.9176, Entropy: 89.3354\n",
      "Epoch[18/20], Step [260/469], Reconst Loss: 21315.2695, KL Div: 92.3770, Entropy: 83.6811\n",
      "Epoch[18/20], Step [270/469], Reconst Loss: 20737.7812, KL Div: 99.6331, Entropy: 83.6334\n",
      "Epoch[18/20], Step [280/469], Reconst Loss: 20955.6113, KL Div: 94.9018, Entropy: 82.6137\n",
      "Epoch[18/20], Step [290/469], Reconst Loss: 19955.3672, KL Div: 87.5802, Entropy: 77.9512\n",
      "Epoch[18/20], Step [300/469], Reconst Loss: 21666.1758, KL Div: 90.0357, Entropy: 85.7446\n",
      "Epoch[18/20], Step [310/469], Reconst Loss: 21240.3262, KL Div: 88.2586, Entropy: 75.8730\n",
      "Epoch[18/20], Step [320/469], Reconst Loss: 20263.7773, KL Div: 90.2877, Entropy: 73.5706\n",
      "Epoch[18/20], Step [330/469], Reconst Loss: 21497.4258, KL Div: 83.4169, Entropy: 84.7817\n",
      "Epoch[18/20], Step [340/469], Reconst Loss: 20368.3730, KL Div: 83.9203, Entropy: 80.1622\n",
      "Epoch[18/20], Step [350/469], Reconst Loss: 19528.3438, KL Div: 92.1637, Entropy: 89.9802\n",
      "Epoch[18/20], Step [360/469], Reconst Loss: 21465.5195, KL Div: 94.8091, Entropy: 82.4920\n",
      "Epoch[18/20], Step [370/469], Reconst Loss: 20611.8379, KL Div: 91.1922, Entropy: 81.1056\n",
      "Epoch[18/20], Step [380/469], Reconst Loss: 21281.5352, KL Div: 87.8812, Entropy: 93.4311\n",
      "Epoch[18/20], Step [390/469], Reconst Loss: 20486.9883, KL Div: 97.7732, Entropy: 79.7068\n",
      "Epoch[18/20], Step [400/469], Reconst Loss: 20478.1875, KL Div: 84.5237, Entropy: 74.8345\n",
      "Epoch[18/20], Step [410/469], Reconst Loss: 20553.1719, KL Div: 83.8687, Entropy: 85.4111\n",
      "Epoch[18/20], Step [420/469], Reconst Loss: 21093.9102, KL Div: 94.0997, Entropy: 76.5240\n",
      "Epoch[18/20], Step [430/469], Reconst Loss: 20862.6094, KL Div: 94.7310, Entropy: 80.3553\n",
      "Epoch[18/20], Step [440/469], Reconst Loss: 21434.8750, KL Div: 82.6359, Entropy: 88.5642\n",
      "Epoch[18/20], Step [450/469], Reconst Loss: 21071.3633, KL Div: 85.0987, Entropy: 83.7347\n",
      "Epoch[18/20], Step [460/469], Reconst Loss: 20696.1445, KL Div: 97.1694, Entropy: 69.4296\n",
      "Epoch[19/20], Step [10/469], Reconst Loss: 20414.4551, KL Div: 110.7303, Entropy: 77.1369\n",
      "Epoch[19/20], Step [20/469], Reconst Loss: 20164.7148, KL Div: 99.3181, Entropy: 92.4828\n",
      "Epoch[19/20], Step [30/469], Reconst Loss: 21345.8945, KL Div: 98.0955, Entropy: 93.1475\n",
      "Epoch[19/20], Step [40/469], Reconst Loss: 21110.1758, KL Div: 94.1121, Entropy: 81.5834\n",
      "Epoch[19/20], Step [50/469], Reconst Loss: 20796.0781, KL Div: 94.1273, Entropy: 88.6555\n",
      "Epoch[19/20], Step [60/469], Reconst Loss: 20587.6211, KL Div: 104.0403, Entropy: 93.7146\n",
      "Epoch[19/20], Step [70/469], Reconst Loss: 20099.3906, KL Div: 90.3354, Entropy: 85.2932\n",
      "Epoch[19/20], Step [80/469], Reconst Loss: 19555.8242, KL Div: 87.6097, Entropy: 87.3247\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[19/20], Step [90/469], Reconst Loss: 21182.2539, KL Div: 103.9029, Entropy: 91.8275\n",
      "Epoch[19/20], Step [100/469], Reconst Loss: 20283.1855, KL Div: 96.8591, Entropy: 86.7553\n",
      "Epoch[19/20], Step [110/469], Reconst Loss: 20578.3203, KL Div: 84.5476, Entropy: 82.0374\n",
      "Epoch[19/20], Step [120/469], Reconst Loss: 20006.3008, KL Div: 101.3569, Entropy: 92.0948\n",
      "Epoch[19/20], Step [130/469], Reconst Loss: 20346.5176, KL Div: 99.3448, Entropy: 85.4978\n",
      "Epoch[19/20], Step [140/469], Reconst Loss: 20472.3730, KL Div: 86.2450, Entropy: 83.6906\n",
      "Epoch[19/20], Step [150/469], Reconst Loss: 21125.3398, KL Div: 107.1608, Entropy: 87.8950\n",
      "Epoch[19/20], Step [160/469], Reconst Loss: 20793.8574, KL Div: 95.7225, Entropy: 99.1880\n",
      "Epoch[19/20], Step [170/469], Reconst Loss: 20609.3125, KL Div: 114.7965, Entropy: 81.0653\n",
      "Epoch[19/20], Step [180/469], Reconst Loss: 20548.9570, KL Div: 91.1246, Entropy: 94.3423\n",
      "Epoch[19/20], Step [190/469], Reconst Loss: 20912.8906, KL Div: 93.4347, Entropy: 94.5446\n",
      "Epoch[19/20], Step [200/469], Reconst Loss: 20954.7695, KL Div: 88.1476, Entropy: 96.2631\n",
      "Epoch[19/20], Step [210/469], Reconst Loss: 20121.4355, KL Div: 93.1487, Entropy: 77.6965\n",
      "Epoch[19/20], Step [220/469], Reconst Loss: 21292.6152, KL Div: 110.0010, Entropy: 91.4566\n",
      "Epoch[19/20], Step [230/469], Reconst Loss: 20991.9805, KL Div: 94.2899, Entropy: 87.0490\n",
      "Epoch[19/20], Step [240/469], Reconst Loss: 21233.6934, KL Div: 95.6317, Entropy: 84.3685\n",
      "Epoch[19/20], Step [250/469], Reconst Loss: 20273.0352, KL Div: 82.7124, Entropy: 87.2758\n",
      "Epoch[19/20], Step [260/469], Reconst Loss: 20455.7246, KL Div: 101.1914, Entropy: 91.3695\n",
      "Epoch[19/20], Step [270/469], Reconst Loss: 19635.4219, KL Div: 98.8794, Entropy: 74.2357\n",
      "Epoch[19/20], Step [280/469], Reconst Loss: 20583.0742, KL Div: 88.6089, Entropy: 82.7415\n",
      "Epoch[19/20], Step [290/469], Reconst Loss: 20085.9746, KL Div: 87.3491, Entropy: 79.8539\n",
      "Epoch[19/20], Step [300/469], Reconst Loss: 19913.4883, KL Div: 100.3599, Entropy: 87.9181\n",
      "Epoch[19/20], Step [310/469], Reconst Loss: 21698.9414, KL Div: 92.2480, Entropy: 99.8023\n",
      "Epoch[19/20], Step [320/469], Reconst Loss: 20550.6758, KL Div: 90.7820, Entropy: 81.5879\n",
      "Epoch[19/20], Step [330/469], Reconst Loss: 21224.0156, KL Div: 95.8199, Entropy: 83.5287\n",
      "Epoch[19/20], Step [340/469], Reconst Loss: 20727.3652, KL Div: 98.1691, Entropy: 82.6533\n",
      "Epoch[19/20], Step [350/469], Reconst Loss: 20298.4941, KL Div: 97.9258, Entropy: 88.6428\n",
      "Epoch[19/20], Step [360/469], Reconst Loss: 20766.5391, KL Div: 100.1744, Entropy: 78.4084\n",
      "Epoch[19/20], Step [370/469], Reconst Loss: 21187.8164, KL Div: 86.1762, Entropy: 82.6687\n",
      "Epoch[19/20], Step [380/469], Reconst Loss: 21037.0625, KL Div: 111.9671, Entropy: 83.9270\n",
      "Epoch[19/20], Step [390/469], Reconst Loss: 21359.2070, KL Div: 83.9296, Entropy: 90.0700\n",
      "Epoch[19/20], Step [400/469], Reconst Loss: 20443.4727, KL Div: 84.2006, Entropy: 82.1610\n",
      "Epoch[19/20], Step [410/469], Reconst Loss: 19822.5273, KL Div: 89.7466, Entropy: 82.1808\n",
      "Epoch[19/20], Step [420/469], Reconst Loss: 20263.9434, KL Div: 99.1512, Entropy: 82.7128\n",
      "Epoch[19/20], Step [430/469], Reconst Loss: 20778.5664, KL Div: 96.4227, Entropy: 88.3178\n",
      "Epoch[19/20], Step [440/469], Reconst Loss: 20579.1426, KL Div: 84.2781, Entropy: 93.8051\n",
      "Epoch[19/20], Step [450/469], Reconst Loss: 21985.2188, KL Div: 90.9780, Entropy: 82.8203\n",
      "Epoch[19/20], Step [460/469], Reconst Loss: 21198.0195, KL Div: 90.9869, Entropy: 86.0213\n",
      "Epoch[20/20], Step [10/469], Reconst Loss: 21108.0488, KL Div: 112.0474, Entropy: 81.6609\n",
      "Epoch[20/20], Step [20/469], Reconst Loss: 20024.1113, KL Div: 110.1758, Entropy: 77.0295\n",
      "Epoch[20/20], Step [30/469], Reconst Loss: 20883.1934, KL Div: 102.4349, Entropy: 101.4266\n",
      "Epoch[20/20], Step [40/469], Reconst Loss: 20883.5391, KL Div: 102.5646, Entropy: 87.3737\n",
      "Epoch[20/20], Step [50/469], Reconst Loss: 21647.3281, KL Div: 106.1839, Entropy: 98.6870\n",
      "Epoch[20/20], Step [60/469], Reconst Loss: 21387.4570, KL Div: 97.1998, Entropy: 98.5294\n",
      "Epoch[20/20], Step [70/469], Reconst Loss: 20632.3281, KL Div: 103.6124, Entropy: 83.6288\n",
      "Epoch[20/20], Step [80/469], Reconst Loss: 20140.0664, KL Div: 106.4311, Entropy: 89.4763\n",
      "Epoch[20/20], Step [90/469], Reconst Loss: 21359.3340, KL Div: 104.9142, Entropy: 87.2186\n",
      "Epoch[20/20], Step [100/469], Reconst Loss: 22011.5820, KL Div: 93.0652, Entropy: 90.7762\n",
      "Epoch[20/20], Step [110/469], Reconst Loss: 20271.0977, KL Div: 91.2435, Entropy: 87.7422\n",
      "Epoch[20/20], Step [120/469], Reconst Loss: 21955.3574, KL Div: 88.3137, Entropy: 100.9580\n",
      "Epoch[20/20], Step [130/469], Reconst Loss: 20958.2793, KL Div: 106.4071, Entropy: 98.7111\n",
      "Epoch[20/20], Step [140/469], Reconst Loss: 20660.7207, KL Div: 106.9648, Entropy: 87.2852\n",
      "Epoch[20/20], Step [150/469], Reconst Loss: 20519.5234, KL Div: 106.5842, Entropy: 81.7287\n",
      "Epoch[20/20], Step [160/469], Reconst Loss: 21497.1895, KL Div: 98.8274, Entropy: 102.2771\n",
      "Epoch[20/20], Step [170/469], Reconst Loss: 20250.9160, KL Div: 107.9630, Entropy: 80.8824\n",
      "Epoch[20/20], Step [180/469], Reconst Loss: 20276.4316, KL Div: 107.0123, Entropy: 84.4221\n",
      "Epoch[20/20], Step [190/469], Reconst Loss: 20928.3379, KL Div: 97.6033, Entropy: 91.0721\n",
      "Epoch[20/20], Step [200/469], Reconst Loss: 21581.8242, KL Div: 114.6373, Entropy: 88.9370\n",
      "Epoch[20/20], Step [210/469], Reconst Loss: 20175.0938, KL Div: 88.3744, Entropy: 92.4840\n",
      "Epoch[20/20], Step [220/469], Reconst Loss: 19693.6035, KL Div: 110.5589, Entropy: 88.7175\n",
      "Epoch[20/20], Step [230/469], Reconst Loss: 20270.8203, KL Div: 91.5681, Entropy: 91.6847\n",
      "Epoch[20/20], Step [240/469], Reconst Loss: 20555.3359, KL Div: 104.0536, Entropy: 89.7302\n",
      "Epoch[20/20], Step [250/469], Reconst Loss: 20948.7969, KL Div: 91.7204, Entropy: 93.9734\n",
      "Epoch[20/20], Step [260/469], Reconst Loss: 20727.1328, KL Div: 91.9137, Entropy: 98.6467\n",
      "Epoch[20/20], Step [270/469], Reconst Loss: 21883.6582, KL Div: 92.7215, Entropy: 96.5494\n",
      "Epoch[20/20], Step [280/469], Reconst Loss: 20215.7852, KL Div: 108.1719, Entropy: 84.9252\n",
      "Epoch[20/20], Step [290/469], Reconst Loss: 19546.5273, KL Div: 97.6386, Entropy: 89.7871\n",
      "Epoch[20/20], Step [300/469], Reconst Loss: 20707.6914, KL Div: 103.1746, Entropy: 96.0190\n",
      "Epoch[20/20], Step [310/469], Reconst Loss: 20566.0625, KL Div: 97.0965, Entropy: 92.7643\n",
      "Epoch[20/20], Step [320/469], Reconst Loss: 21339.2656, KL Div: 95.2210, Entropy: 102.5494\n",
      "Epoch[20/20], Step [330/469], Reconst Loss: 19311.2695, KL Div: 98.0822, Entropy: 85.7878\n",
      "Epoch[20/20], Step [340/469], Reconst Loss: 20946.5586, KL Div: 102.3400, Entropy: 97.9771\n",
      "Epoch[20/20], Step [350/469], Reconst Loss: 21460.4863, KL Div: 99.7173, Entropy: 100.6582\n",
      "Epoch[20/20], Step [360/469], Reconst Loss: 21611.0625, KL Div: 97.4595, Entropy: 91.4463\n",
      "Epoch[20/20], Step [370/469], Reconst Loss: 20949.4648, KL Div: 112.8044, Entropy: 86.5038\n",
      "Epoch[20/20], Step [380/469], Reconst Loss: 21320.6113, KL Div: 101.2968, Entropy: 99.6613\n",
      "Epoch[20/20], Step [390/469], Reconst Loss: 20483.5117, KL Div: 95.0204, Entropy: 97.1679\n",
      "Epoch[20/20], Step [400/469], Reconst Loss: 21175.1055, KL Div: 95.4684, Entropy: 99.1422\n",
      "Epoch[20/20], Step [410/469], Reconst Loss: 20797.7344, KL Div: 119.8248, Entropy: 93.5415\n",
      "Epoch[20/20], Step [420/469], Reconst Loss: 20916.1172, KL Div: 97.2031, Entropy: 98.2701\n",
      "Epoch[20/20], Step [430/469], Reconst Loss: 20747.8477, KL Div: 101.9720, Entropy: 79.6816\n",
      "Epoch[20/20], Step [440/469], Reconst Loss: 20071.9180, KL Div: 109.0199, Entropy: 85.2509\n",
      "Epoch[20/20], Step [450/469], Reconst Loss: 20368.3750, KL Div: 93.8682, Entropy: 96.0765\n",
      "Epoch[20/20], Step [460/469], Reconst Loss: 21721.7891, KL Div: 99.1561, Entropy: 104.6075\n"
     ]
    }
   ],
   "source": [
    "# Hyper-parameters\n",
    "num_epochs = 20\n",
    "learning_rate = 1e-3\n",
    "beta = 20\n",
    "C_z_fin=100\n",
    "C_c_fin=100\n",
    "\n",
    "model_G = VAE_Gumbel(z_dim = z_dim).to(device)\n",
    "optimizer = torch.optim.Adam(model_G.parameters(), lr=learning_rate)\n",
    "\n",
    "nmi, labels_est = train_G_modified_loss(model_G, data_loader, num_epochs=num_epochs, beta=beta, C_z_fin=C_z_fin, C_c_fin=C_c_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbb400a7eb0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atm3bZb9PU1OTmpo+v29bU1PTRWsA/s4wDBVX1rf2fhyu0IfHzulCs8P1vsUiTRkSq7mj4zVvdJymDIlltVDAg7gVRiorK+VwOJSYmNhue2Jiog4ePNjhPtu3b9czzzyjTz75pNvfJzc3Vw899JA7pQHwM3VNLdpRVKmth1vHfpSea78UekJUyMXwEa+rR8Z169kwAMzRp7Npamtrdfvtt+vpp59WXFxct/dbsWKFcnJyXF/X1NQoNTW1L0oE4AWaHU6dONegYxX1OmSr0bYjlSooOa+WL4w8DQ6wKiNtgOaNjtfc0fEamxTFMuWAl3ArjMTFxSkgIEBlZWXttpeVlSkpKemS9kePHtXx48f1ta99zbXN6Wx9wmNgYKAOHTqkESNGXLJfSEiIQkJC3CkNgJczDEMVtU3tHgBXXFmvY5X1OnGuwfUk3C8aHhehuaPiNHd0vGanD+JpsYCXcuuTGxwcrBkzZigvL0+33nqrpNZwkZeXp3vvvfeS9mPHjtWePXvabfvFL36h2tpa/eEPf6C3A/BDDfaW1pBRcfFV2Ro6iivqVdvU0ul+4cEBGh4XofT4SM0aPlDzRsVr6KDwfqwcQF9x+58ROTk5WrZsmTIyMjRr1iytXr1a9fX1Wr58uSRp6dKlSklJUW5urkJDQzVx4sR2+8fGxkrSJdsB+A6H09Cp8xd0rLKuXeA4VlGvM9WNne5ntUhDBoQrPT5C6XGRGh4foREXA0hidAi3XQAf5XYYWbRokSoqKrRy5UrZbDZNnTpVGzdudA1qPXHihKxWRqkD/qaovE5v7zmjTQfKdNBWK3uLs9O2A8KDlB4fqfS4CA2/GDxGxEdo6KBwhQSyxgfgb9xeZ8QMrDMCeB7DMHTQVqu399r09p4zOlJe1+794ACr0uLClR4XqfT4CNctlvS4CGa2AH6iT9YZAeDfDMPQnlPVrgBy/GyD672gAIuuGhmnhROTlJUep5QBYTzPBUC3EEYAdMnpNPRx6Xm9vcemt/fadKrq8/U8ggOtmjc6XgsnJun6cYmKCeOJtgDcRxgBcAmH09Cu4nPauPeMNu6ztXuSbVhQgOaPTdBNE5N03dgERTKdFsAV4m8RAJJaFxbLP3pWb++1adN+myrr7K73IkMClT0uQTdNHKx5o+MVFswgUwC9hzAC+LGmFoc+KKrUW3ts2rS/rN3TbGPCgnTj+EQtnJSkq0bGMcsFQJ8hjAB+prHZoS2HKvT23jN670B5u4XGBkUE68YJSfrKpCTNTh+kIB4mB6AfEEYAP+JwGvrun3eqoOS8a1tidIhumpCkhZMGa2baQGbAAOh3hBHAj/zzs9MqKDmviOAALZk1VAsnDda01FhZCSAATEQYAfxEi8OpP7x7RJL04+tG6p7rRppcEQC04oYw4Cfe/OS0jlXWa0B4kJbNSTO7HABwIYwAfqDZ4dQf32vtFfnhvBGsDQLAoxBGAD/weuEplZxt0KCIYC3NGmZ2OQDQDmEE8HH2Fqf+kNfaK/Kja0coPJheEQCehTAC+LhXC0p1quqC4qNC9L3Z9IoA8DyEEcCHNbU4tOa9IknSPdeOUGgQq6gC8DyEEcCHbdhdqjPVjUqKDtXiWUPNLgcAOkQYAXxUY7NDazdf7BWZP5JeEQAeizAC+Ki/7jyhspompcSG6baMIWaXAwCdIowAPqjB3qIntrT2itw3fyRP3AXg0QgjgA964cMSVdbZlTowTN+aQa8IAM9GGAF8TH1Ti57cekyS9O/zRykogI85AM/G31KAj1m/47jO1ds1PC5C35iWYnY5AHBZhBHAh9Q2NutP77f2ivzk+lEKpFcEgBfgbyrAhzz7wXFVX2jWiPgIfW1KstnlAEC3EEYAH1F9oVlPb2vtFbk/e7QCrBaTKwKA7iGMAD7ime3Fqm1s0ejESN08abDZ5QBAtxFGAB9wvt6udduLJUn/kT1aVnpFAHgRwgjgA57edkx1TS0aPzhaCyYkmV0OALiFMAJ4ubN1TVq/47gk6T9uoFcEgPchjABe7k/vH1OD3aFJKTHKHpdgdjkA4DbCCODFymsb9Vz+cUlSzg2jZbHQKwLA+xBGAC/25JZjamx2ampqrK4dE292OQDQI4QRwEuV1TTqhZ0lkqSf3kivCADvRRgBvNT/2Vwke4tTM9MG6OqRcWaXAwA9RhgBvNDpqgt6aVeppNYZNPSKAPBmhBHAC63ZXCS7w6nZ6QM1ZwS9IgC8G2EE8DKl5xr0yu7WXpGcG8aYXA0AXDnCCOBl1rxXpBanoWtGxWnW8IFmlwMAV4wwAniRkrP1+lvhSUmtT+YFAF9AGAG8yB/ziuRwGrp2TLxmDBtgdjkA0CsII4CXOFpRp9c/bu0V+Q96RQD4EMII4CX+mHdETkPKHpeoKamxZpcDAL2GMAJ4gSNltfrHp6clSfdnjzK5GgDoXYQRwAuszjsiw5BumpCkiSkxZpcDAL2KMAJ4uANnavSvz85Iku6/gV4RAL6HMAJ4uNXvHpYk3Tx5sMYmRZtcDQD0PsII4MH2nqrWO/vKZLFI/8FYEQA+ijACeLC2XpFbpiRrZEKUydUAQN8gjAAe6tPSKr17oFxWi/Tv19MrAsB3EUYAD/X7Ta29It+YNkTp8ZEmVwMAfYcwAniggpJz2nq4QgFWi35CrwgAH0cYATzQ/950RJL0nRlDNHRQuMnVAEDfCjS7AACtWhxOHT9brw+Kzmp7UaWCAiy657qRZpcFAH2OMAKY4Fy9XQfP1OiArfbinzU6XFYne4vT1ea2jFSlDqRXBIDvI4wAfajZ4dSxinodtNVo/5kaHTxTq4O2GpXVNHXYPjw4QGOSopQxbIB+wpN5AfgJwgjQSyrrmnTgYuA4YGv9s6i8TnaHs8P2wwaFa2xSlMYmRWvc4GiNGxyl1AHhslot/Vw5AJiLMAL0wLGKOn1SWqWDtlodOFOjA2dqVVnXcW9HZEhga+gYHKVxg6M1NilaY5KiFBnCxw8AJMII4La/fFiilW/ulWG0326xSGmDIjRucGtvx9ik1vAxZECYLBZ6OwCgM4QRwA2bD5Vr1cUgMm1orCanxGjs4NbbLKMTIxUezEcKANzF35xANx04U6N7/1oopyF9e8YQPfrtyfR4AEAv6NGiZ2vXrlVaWppCQ0OVmZmpXbt2ddr2tddeU0ZGhmJjYxUREaGpU6fqL3/5S48LBsxQXtOou9bvVr3dodnpA/Wbb0wiiABAL3E7jGzYsEE5OTlatWqVCgsLNWXKFC1YsEDl5eUdth84cKD+8z//U/n5+frss8+0fPlyLV++XO+8884VFw/0hwt2h77//Ec6Xd2o9LgIPfm9GQoOZPFiAOgtFsP48jC8rmVmZmrmzJlas2aNJMnpdCo1NVX33XefHnzwwW4dY/r06br55pv18MMPd6t9TU2NYmJiVF1drejoaHfKBa6I02nox38t1MZ9Ng0ID9LrP75KaXERZpcFAF6hu7+/3frnnd1uV0FBgbKzsz8/gNWq7Oxs5efnX3Z/wzCUl5enQ4cOae7cuZ22a2pqUk1NTbsXYIbfvnNQG/fZFBxg1Z+WZhBEAKAPuBVGKisr5XA4lJiY2G57YmKibDZbp/tVV1crMjJSwcHBuvnmm/X444/rhhtu6LR9bm6uYmJiXK/U1FR3ygR6xcu7TuiprcckSb/79mTNTBtockUA4Jv65cZ3VFSUPvnkE+3evVv/9V//pZycHG3ZsqXT9itWrFB1dbXrVVpa2h9lAi4fFFXqF2/slST95PpRunVaiskVAYDvcmtqb1xcnAICAlRWVtZue1lZmZKSkjrdz2q1auTI1qePTp06VQcOHFBubq6uvfbaDtuHhIQoJCTEndKAXlNUXqv/74UCtTgN3TI1WfdnjzK7JADwaW71jAQHB2vGjBnKy8tzbXM6ncrLy1NWVla3j+N0OtXU1PHS2YCZKuuatHz9btU2tihj2AD99lusJQIAfc3tRc9ycnK0bNkyZWRkaNasWVq9erXq6+u1fPlySdLSpUuVkpKi3NxcSa3jPzIyMjRixAg1NTXprbfe0l/+8hc98cQTvXsmwBVqbHboB89/pNJzFzR0YLieun2GQoMCzC4LAHye22Fk0aJFqqio0MqVK2Wz2TR16lRt3LjRNaj1xIkTslo/73Cpr6/Xj3/8Y508eVJhYWEaO3asXnjhBS1atKj3zgK4Qk6nof/1t89UeKJK0aGBWnfHTA2K5FYhAPQHt9cZMQPrjKCv/f5/DumP7xUp0GrR83fO0pyRcWaXBABer0/WGQF80d8LTuqP7xVJkn7zzUkEEQDoZ4QR+LWdx87qwdc+kyT96NoRui2DNW0AoL8RRuC3iivr9cMXCtTsMPSVSUn6XzeOMbskAPBLhBH4pfP1dt25freqGpo1JTVWv79tqqxWpvACgBkII/A79hanfvhCgYor65USG6anlzKFFwDMRBiBXzEMQyte26NdxecUGdI6hTchKtTssgDArxFG4FfWbi7S3wtPKsBq0drvTteYpCizSwIAv0cYgd/4v5+e1mP/c1iS9KuvT9C80fEmVwQAkAgj8BMFJef101c/lSTddfVw3T57mMkVAQDaEEbg80rPNegHz38ke4tT2eMS9fOvjDO7JADAFxBG4NOqLzRr+frdOltv14TkaP1h8VQFMIUXADwKYQQ+q9nh1D1/LVRReZ2SokP1zLKZighx+9mQAIA+RhiBTzIMQyvf3KvtRZUKDw7Qn5dlKCmGKbwA4IkII/BJT287ppd2lcpqkf64eJompsSYXRIAoBOEEficU1UX9MjbByVJv7h5vLLHJ5pcEQCgK4QR+JyNe21yGtLMtAFaflWa2eUAAC6DMAKfs3HvGUnSVyYNlsXCzBkA8HSEEfiU8tpGfVRyXpK0YEKSydUAALqDMAKf8j/7ymQY0pTUWCXHhpldDgCgGwgj8Cnv7LNJkhZOpFcEALwFYQQ+o6rBrvyjZyVJN3GLBgC8BmEEPuPdA+VqcRoamxSltLgIs8sBAHQTYQQ+o20WzU3cogEAr0IYgU+oa2rR+0cqJUkLJw42uRoAgDsII/AJmw+Wy97i1PC4CI1OjDS7HACAGwgj8AkbL86iuWliEgudAYCXIYzA6zU2O7T5YLkkZtEAgDcijMDrbTtSqQa7Q8kxoZo8hKfzAoC3IYzA6719cRbNAm7RAIBXIozAqzU7nHp3f5kkbtEAgLcijMCr5R89q5rGFsVFBisjbaDZ5QAAeoAwAq/WNovmhvFJCrByiwYAvBFhBF7L4TT0PzwYDwC8HmEEXqug5Lwq6+yKDg3U7PRBZpcDAOghwgi8VtssmuzxiQoO5EcZALwVf4PDKxmGoXf2Xlx1lVk0AODVCCPwSp+drNbp6kaFBwdo7uh4s8sBAFwBwgi8UtssmuvGJCg0KMDkagAAV4IwAq9jGIY27v38wXgAAO9GGIHXOVxWp+LKegUHWnXd2ASzywEAXCHCCLxO2yyauaPiFBkSaHI1AIArRRiB12m7RbOAWTQA4BMII/AqxyvrddBWqwCrRTeMTzS7HABALyCMwKu0zaLJSh+k2PBgk6sBAPQGwgi8ytvMogEAn0MYgdc4XXVBn5ZWyWKRbpzALRoA8BWEEXiNtif0ZgwboISoUJOrAQD0FsIIvMbbzKIBAJ9EGIFXqKxr0u7j5yQxXgQAfA1hBF5h0/4yOQ1pUkqMhgwIN7scAEAvIozAK/AsGgDwXYQReLzqC83acbRSEmEEAHwRYQQe772DZWp2GBqdGKkR8ZFmlwMA6GWEEXi8t/dcvEXDLBoA8EmEEXi0BnuLth6ukCTdNHGwydUAAPoCYQQebcuhCjW1ODV0YLjGDY4yuxwAQB8gjMCjtc2iWTgxSRaLxeRqAAB9gTACj9XU4tB7B8slSQuYRQMAPoswAo/1QVGl6ppalBgdoqlDYs0uBwDQR3oURtauXau0tDSFhoYqMzNTu3bt6rTt008/rWuuuUYDBgzQgAEDlJ2d3WV7oM0XZ9FYrdyiAQBf5XYY2bBhg3JycrRq1SoVFhZqypQpWrBggcrLyztsv2XLFi1ZskSbN29Wfn6+UlNTdeONN+rUqVNXXDx8V4vDqU0HyiRxiwYAfJ3FMAzDnR0yMzM1c+ZMrVmzRpLkdDqVmpqq++67Tw8++OBl93c4HBowYIDWrFmjpUuXdut71tTUKCYmRtXV1YqOjnanXHipD4oq9d0/79TAiGDt+vn1CgzgjiIAeJvu/v526294u92ugoICZWdnf34Aq1XZ2dnKz8/v1jEaGhrU3NysgQMHdtqmqalJNTU17V7wL22zaG4Yl0gQAQAf59bf8pWVlXI4HEpMTGy3PTExUTabrVvHeOCBB5ScnNwu0HxZbm6uYmJiXK/U1FR3yoSXczoNvbPv4niRSdyiAQBf16//5HzkkUf08ssv6/XXX1doaGin7VasWKHq6mrXq7S0tB+rhNk+Lj2v8tomRYUEas6IQWaXAwDoY4HuNI6Li1NAQIDKysrabS8rK1NSUtf/gn3sscf0yCOP6N1339XkyZO7bBsSEqKQkBB3SoMPaZtFc/24BIUEBphcDQCgr7nVMxIcHKwZM2YoLy/Ptc3pdCovL09ZWVmd7ve73/1ODz/8sDZu3KiMjIyeVwufZxiGNrbdomEWDQD4Bbd6RiQpJydHy5YtU0ZGhmbNmqXVq1ervr5ey5cvlyQtXbpUKSkpys3NlST99re/1cqVK/Xiiy8qLS3NNbYkMjJSkZE8Dh7t7Ttdo5PnLyg0yKp5oxPMLgcA0A/cDiOLFi1SRUWFVq5cKZvNpqlTp2rjxo2uQa0nTpyQ1fp5h8sTTzwhu92ub3/72+2Os2rVKv3qV7+6surhc9pm0Vw7OkFhwdyiAQB/4PY6I2ZgnRH/cf1/b9HRinr9YfFU3TI1xexyAABXoE/WGQH6UlF5rY5W1CsowKLrxnKLBgD8BWEEHqNtFs3VI+MUHRpkcjUAgP5CGIHHYBYNAPgnwgg8womzDdp3ukZWi3TDeMIIAPgTwgg8Qtvy75nDB2lgRLDJ1QAA+hNhBB7h7b1nJEkLeRYNAPgdwghMV1bTqMITVZKkG7lFAwB+hzAC07Xdopk+NFZJMZ0/QBEA4JsIIzBd26qrzKIBAP9EGIGpztXbtbP4nCTppgmDTa4GAGAGwghM9e7+MjmchsYPjtbQQeFmlwMAMAFhBKZqW+hsIbdoAMBvEUZgmtrGZm0/UimJ8SIA4M8IIzDNewfLZXc4NSI+QqMSo8wuBwBgEsIITMMsGgCARBiBSS7YHdpyqEKStHAis2gAwJ8RRmCKrYcrdKHZoZTYME1Ijja7HACAiQgjMEXbqqs3TUySxWIxuRoAgJkII+h31Q3NrvEiX+HBeADg9wgj6HevFpTqQrNDY5OiNH3oALPLAQCYjDCCfuVwGnou/7gkadmcNG7RAAAII+hf7x0sV+m5C4oJC9KtU1PMLgcA4AEII+hXz+04LklaPCtVYcEB5hYDAPAIhBH0myNltdpeVCmrRbp99jCzywEAeAjCCPrN+ou9IjeMT9SQATyhFwDQijCCflF9oVmvFZ6SJN0xZ7jJ1QAAPAlhBP3i1Y9ap/OOSYzS7PSBZpcDAPAghBH0uS9O573jKqbzAgDaI4ygz21mOi8AoAuEEfS5toGri2cynRcAcCnCCPrUF6fzfo/pvACADhBG0KfaxorcMD5RqQOZzgsAuBRhBH2m+kKz/l7QOp132Zw0c4sBAHgswgj6zBen82alDzK7HACAhyKMoE84nIaezy+RxNN5AQBdI4ygT2w+WK4T5xpap/NOSza7HACAByOMoE+0DVxdPDNV4cGB5hYDAPBohBH0uiNltdp2hOm8AIDuIYyg17X1imSPYzovAODyCCPoVe2ezntVmrnFAAC8AmEEverVj0rVYGc6LwCg+wgj6DVM5wUA9ARhBL1myyGm8wIA3EcYQa9pezrvIqbzAgDcQBhBrygq/3w67+1M5wUAuIEwgl7x3I7WsSJM5wUAuIswgitWfaFZfy88KUm6g6fzAgDcRBjBFWubzjs6MVJZI5jOCwBwD2EEV+SL03nvmDOc6bwAALcRRnBF2qbzRocGMp0XANAjhBFckbbpvItnDWU6LwCgRwgj6LG26bwWpvMCAK4AYQQ9xnReAEBvIIygR2oaP5/Ou5zpvACAK0AYQY+8+tFJpvMCAHoFYQRuczoNPZ9/XBJP5wUAXDnCCNy25XC5Ss62Tuf9xrQUs8sBAHg5wgjc9uwHxyXxdF4AQO8gjMAtReV1rum8S7PSzC4HAOADehRG1q5dq7S0NIWGhiozM1O7du3qtO2+ffv0rW99S2lprWMLVq9e3dNa4QHaxoownRcA0FvcDiMbNmxQTk6OVq1apcLCQk2ZMkULFixQeXl5h+0bGhqUnp6uRx55RElJSVdcMMxT09isvxXwdF4AQO9yO4z8/ve/1913363ly5dr/PjxevLJJxUeHq5169Z12H7mzJl69NFHtXjxYoWEhFxxwTBP23TeUQmRmsN0XgBAL3ErjNjtdhUUFCg7O/vzA1itys7OVn5+fq8V1dTUpJqamnYvmOuL03nvuIrpvACA3uNWGKmsrJTD4VBiYmK77YmJibLZbL1WVG5urmJiYlyv1NTUXjs2eobpvACAvuKRs2lWrFih6upq16u0tNTskvwe03kBAH3Frd8qcXFxCggIUFlZWbvtZWVlvTo4NSQkhPElHoTpvACAvuRWz0hwcLBmzJihvLw81zan06m8vDxlZWX1enHwDG1jRa4fy3ReAEDvc7u/PScnR8uWLVNGRoZmzZql1atXq76+XsuXL5ckLV26VCkpKcrNzZXUOuh1//79rv8+deqUPvnkE0VGRmrkyJG9eCroC1+czrv8qjRziwEA+CS3w8iiRYtUUVGhlStXymazaerUqdq4caNrUOuJEydktX7e4XL69GlNmzbN9fVjjz2mxx57TPPmzdOWLVuu/AzQp/7GdF4AQB+zGIZhmF3E5dTU1CgmJkbV1dWKjo42uxy/4XQauu6/t6jkbIP+/1sn6nuzh5ldEgDAi3T397dHzqaBZ2ibzhsVGqhvTmc6LwCgbxBG0Kn1O0okSYsymM4LAOg7hBF0qKi8Tu8frmA6LwCgzxFG0KEXPmztFbl+bKKGDmI6LwCg7xBGcInGZodeK2ydznt7FoNWAQB9izCCS7y154xqGluUEhuma0bGmV0OAMDHEUZwiZd2nZAkLZmVKquVp/MCAPoWYQTtHCmr1e7j5xVgteg7GTwtGQDQ9wgjaOelXa1PSJ4/NkGJ0aEmVwMA8AeEEbg0Njv02setA1f/bdZQk6sBAPgLwghc3tlnU1VDs5JjQjV3dLzZ5QAA/ARhBC4v7mwduHrbzFQFMHAVANBPCCOQJB2rqNPO4nOyWqTbGLgKAOhHhBFIkl7e3Tpw9boxCUqODTO5GgCAPyGMQE0tDv2toHXg6mIGrgIA+hlhBNq0v0zn6u1KjA7RdWMYuAoA6F+EEbhWXF2UkarAAH4kAAD9i988fq7kbL0+KDori6V1Fg0AAP2NMOLn2gauzh0VryEDwk2uBgDgjwgjfsze4tSrH7WGkSUMXAUAmIQw4sfyDpSpss6uuMgQXT8uwexyAAB+ijDix166eIvmtowhCmLgKgDAJPwG8lOl5xq07UiFJGnxTG7RAADMQxjxUxt2l8owpKtHxmnoIAauAgDMQxjxQy0Op15h4CoAwEMQRvzQewfLVV7bpEERwbphfKLZ5QAA/BxhxA+1rbj67RlDFBzIjwAAwFz8JvIzp6ouaOvh1oGri1hxFQDgAQgjfuaV3aVyGlJW+iClx0eaXQ4AAIQRf+JwGq6Bq4tn0SsCAPAMhBE/svVwuc5UN2pAeJAWTEgyuxwAACQRRvzKiztbe0W+NX2IQoMCTK4GAIBWhBE/Yatu1HsHyyRxiwYA4FkII37i1Y9aB67OShuokQlRZpcDAIALYcQPOJyGXr74ULwlmfSKAAA8C2HED2w7UqFTVRcUHRqohRMHm10OAADtEEb8wMu7WntFvsnAVQCAByKM+Ljymka9e6B14CoPxQMAeCLCiI97teCkWpyGpg+N1ZgkBq4CADwPYcSHOZ2GNrQNXKVXBADgoQgjPmzH0bM6ca5BUaGB+urkZLPLAQCgQ4QRH/bSrhOSpG9MS1FYMANXAQCeiTDioyrrmvQ/+22SpMUzuUUDAPBchBEf9feCk2p2GJqSGqvxydFmlwMAQKcIIz7IMAzXLZp/4zk0AAAPRxjxQfnHzur42QZFBAcwcBUA4PEIIz6obcXVW6alKCIk0ORqAADoGmHEx5yrt2vj3taBq//G2iIAAC9AGPExrxWelN3h1MSUaE1MiTG7HAAALosw4kO+OHCVFVcBAN6CMOJDdh8/r6MV9QoPDtDXpzBwFQDgHQgjPqStV+Rrk5MVFRpkcjUAAHQPYcRHVDXY9a89ZyRJSzK5RQMA8B6EER/x+senZG9xatzgaE0ZwsBVAID3IIz4gPYDV1NlsVhMrggAgO4jjPiAwhNVOlxWp9Agq26ZmmJ2OQAAuIUw4gPaekW+OjlZMWEMXAUAeBfCiJervtCsf352WlLrLRoAALwNYcTL/eOTU2psdmp0YqSmDx1gdjkAALitR2Fk7dq1SktLU2hoqDIzM7Vr164u27/66qsaO3asQkNDNWnSJL311ls9KhbtGYahv+78fMVVBq4CALyR24903bBhg3JycvTkk08qMzNTq1ev1oIFC3To0CElJCRc0n7Hjh1asmSJcnNz9dWvflUvvviibr31VhUWFmrixIm9chI99e8vfazDZbWKjwpRfFSIEqJCL/4Z0u7PyJBAj/xF/+nJah201So40KpvTGPgKgDAO1kMwzDc2SEzM1MzZ87UmjVrJElOp1Opqam677779OCDD17SftGiRaqvr9c///lP17bZs2dr6tSpevLJJ7v1PWtqahQTE6Pq6mpFR0e7U26XvvKHbdp/puay7cKCAjoMKW3hpW3boMgQBVj7L7Q8+PfP9PLuUn1jWor+96Kp/fZ9AQDoju7+/narZ8Rut6ugoEArVqxwbbNarcrOzlZ+fn6H++Tn5ysnJ6fdtgULFuiNN97o9Ps0NTWpqanJ9XVNzeUDQ0/8cclUna5qVHltkypqm1Re23jxzyZVXvyzrqlFF5odOnGuQSfONXR5PKtFGhjRPrREhASqrzpV/vFp28BVVlwFAHgvt8JIZWWlHA6HEhMT221PTEzUwYMHO9zHZrN12N5ms3X6fXJzc/XQQw+5U1qPjEyI0siEqC7bNNhbVOEKK5eGlrY/z9Y1yWlIlXVNqqxrks70efmSpBHxEZqZxsBVAID3cnvMSH9YsWJFu96UmpoapaaaM201PDhQwwYFatigiC7bOZyGzta3Dy0VtU1qsLf0WW1Wi0VfnZzskeNZAADoLrfCSFxcnAICAlRWVtZue1lZmZKSkjrcJykpya32khQSEqKQkBB3SjNdgNWihKhQJUSFaoLZxQAA4EXcmtobHBysGTNmKC8vz7XN6XQqLy9PWVlZHe6TlZXVrr0kbdq0qdP2AADAv7h9myYnJ0fLli1TRkaGZs2apdWrV6u+vl7Lly+XJC1dulQpKSnKzc2VJP3kJz/RvHnz9N///d+6+eab9fLLL+ujjz7Sn/70p949EwAA4JXcDiOLFi1SRUWFVq5cKZvNpqlTp2rjxo2uQaonTpyQ1fp5h8ucOXP04osv6he/+IV+/vOfa9SoUXrjjTdMX2MEAAB4BrfXGTFDX60zAgAA+k53f3/zbBoAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCq3l4M3Q9sisTU1NSZXAgAAuqvt9/blFnv3ijBSW1srSUpNTTW5EgAA4K7a2lrFxMR0+r5XPJvG6XTq9OnTioqKksVi6bXj1tTUKDU1VaWlpX7xzBt/Ol/O1Xf50/lyrr7LX87XMAzV1tYqOTm53UN0v8wrekasVquGDBnSZ8ePjo726R+GL/On8+VcfZc/nS/n6rv84Xy76hFpwwBWAABgKsIIAAAwlV+HkZCQEK1atUohISFml9Iv/Ol8OVff5U/ny7n6Ln8738vxigGsAADAd/l1zwgAADAfYQQAAJiKMAIAAExFGAEAAKby+TCydu1apaWlKTQ0VJmZmdq1a1eX7V999VWNHTtWoaGhmjRpkt56661+qvTK5ObmaubMmYqKilJCQoJuvfVWHTp0qMt91q9fL4vF0u4VGhraTxX33K9+9atL6h47dmyX+3jrdU1LS7vkXC0Wi+65554O23vbNX3//ff1ta99TcnJybJYLHrjjTfavW8YhlauXKnBgwcrLCxM2dnZOnLkyGWP6+7nvj90da7Nzc164IEHNGnSJEVERCg5OVlLly7V6dOnuzxmTz4L/eFy1/WOO+64pO6bbrrpssf1xOsqXf58O/oMWywWPfroo50e01OvbV/x6TCyYcMG5eTkaNWqVSosLNSUKVO0YMEClZeXd9h+x44dWrJkie666y59/PHHuvXWW3Xrrbdq7969/Vy5+7Zu3ap77rlHH374oTZt2qTm5mbdeOONqq+v73K/6OhonTlzxvUqKSnpp4qvzIQJE9rVvX379k7bevN13b17d7vz3LRpkyTpO9/5Tqf7eNM1ra+v15QpU7R27doO3//d736nP/7xj3ryySe1c+dORUREaMGCBWpsbOz0mO5+7vtLV+fa0NCgwsJC/fKXv1RhYaFee+01HTp0SF//+tcve1x3Pgv95XLXVZJuuummdnW/9NJLXR7TU6+rdPnz/eJ5njlzRuvWrZPFYtG3vvWtLo/ride2zxg+bNasWcY999zj+trhcBjJyclGbm5uh+1vu+024+abb263LTMz0/jhD3/Yp3X2hfLyckOSsXXr1k7bPPvss0ZMTEz/FdVLVq1aZUyZMqXb7X3puv7kJz8xRowYYTidzg7f99ZrahiGIcl4/fXXXV87nU4jKSnJePTRR13bqqqqjJCQEOOll17q9Djufu7N8OVz7ciuXbsMSUZJSUmnbdz9LJiho3NdtmyZccstt7h1HG+4robRvWt7yy23GPPnz++yjTdc297ksz0jdrtdBQUFys7Odm2zWq3Kzs5Wfn5+h/vk5+e3ay9JCxYs6LS9J6uurpYkDRw4sMt2dXV1GjZsmFJTU3XLLbdo3759/VHeFTty5IiSk5OVnp6u7373uzpx4kSnbX3lutrtdr3wwgu68847u3xgpLde0y8rLi6WzWZrd+1iYmKUmZnZ6bXryefeU1VXV8tisSg2NrbLdu58FjzJli1blJCQoDFjxuhHP/qRzp4922lbX7quZWVl+te//qW77rrrsm299dr2hM+GkcrKSjkcDiUmJrbbnpiYKJvN1uE+NpvNrfaeyul06v7779dVV12liRMndtpuzJgxWrdund5880298MILcjqdmjNnjk6ePNmP1bovMzNT69ev18aNG/XEE0+ouLhY11xzjWprazts7yvX9Y033lBVVZXuuOOOTtt46zXtSNv1cefa9eRz74kaGxv1wAMPaMmSJV0+RM3dz4KnuOmmm/T8888rLy9Pv/3tb7V161YtXLhQDoejw/a+cl0l6bnnnlNUVJS++c1vdtnOW69tT3nFU3vhnnvuuUd79+697P3FrKwsZWVlub6eM2eOxo0bp6eeekoPP/xwX5fZYwsXLnT99+TJk5WZmalhw4bplVde6da/NrzVM888o4ULFyo5ObnTNt56TfG55uZm3XbbbTIMQ0888USXbb31s7B48WLXf0+aNEmTJ0/WiBEjtGXLFl1//fUmVtb31q1bp+9+97uXHVjurde2p3y2ZyQuLk4BAQEqKytrt72srExJSUkd7pOUlORWe09077336p///Kc2b96sIUOGuLVvUFCQpk2bpqKioj6qrm/ExsZq9OjRndbtC9e1pKRE7777rr7//e+7tZ+3XlNJruvjzrXryefek7QFkZKSEm3atMntR8tf7rPgqdLT0xUXF9dp3d5+Xdts27ZNhw4dcvtzLHnvte0unw0jwcHBmjFjhvLy8lzbnE6n8vLy2v3L8YuysrLatZekTZs2ddrekxiGoXvvvVevv/663nvvPQ0fPtztYzgcDu3Zs0eDBw/ugwr7Tl1dnY4ePdpp3d58Xds8++yzSkhI0M033+zWft56TSVp+PDhSkpKanftampqtHPnzk6vXU8+956iLYgcOXJE7777rgYNGuT2MS73WfBUJ0+e1NmzZzut25uv6xc988wzmjFjhqZMmeL2vt56bbvN7BG0fenll182QkJCjPXr1xv79+83fvCDHxixsbGGzWYzDMMwbr/9duPBBx90tf/ggw+MwMBA47HHHjMOHDhgrFq1yggKCjL27Nlj1il0249+9CMjJibG2LJli3HmzBnXq6GhwdXmy+f70EMPGe+8845x9OhRo6CgwFi8eLERGhpq7Nu3z4xT6Laf/vSnxpYtW4zi4mLjgw8+MLKzs424uDijvLzcMAzfuq6G0TprYOjQocYDDzxwyXvefk1ra2uNjz/+2Pj4448NScbvf/974+OPP3bNIHnkkUeM2NhY48033zQ+++wz45ZbbjGGDx9uXLhwwXWM+fPnG48//rjr68t97s3S1bna7Xbj61//ujFkyBDjk08+afcZbmpqch3jy+d6uc+CWbo619raWuNnP/uZkZ+fbxQXFxvvvvuuMX36dGPUqFFGY2Oj6xjecl0N4/I/x4ZhGNXV1UZ4eLjxxBNPdHgMb7m2fcWnw4hhGMbjjz9uDB061AgODjZmzZplfPjhh6735s2bZyxbtqxd+1deecUYPXq0ERwcbEyYMMH417/+1c8V94ykDl/PPvusq82Xz/f+++93/b9JTEw0vvKVrxiFhYX9X7ybFi1aZAwePNgIDg42UlJSjEWLFhlFRUWu933puhqGYbzzzjuGJOPQoUOXvOft13Tz5s0d/ty2nZPT6TR++ctfGomJiUZISIhx/fXXX/L/YdiwYcaqVavabevqc2+Wrs61uLi408/w5s2bXcf48rle7rNglq7OtaGhwbjxxhuN+Ph4IygoyBg2bJhx9913XxIqvOW6Gsblf44NwzCeeuopIywszKiqqurwGN5ybfuKxTAMo0+7XgAAALrgs2NGAACAdyCMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBU/w/zTfbea9yR8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hMF3LMn1Mtza"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reconstruction(model_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tR2sn2l7Mtzg"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conditional_generation(model_G, fix_number=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KoF28npFMtzt"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conditional_generation(model_G, fix_number=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "VAE_clustering_empty.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "dldiy",
   "language": "python",
   "name": "dldiy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}