diff --git a/bayesian/normalizing_flows/vae.ipynb b/bayesian/normalizing_flows/vae.ipynb
index af39412..a4d8424 100644
--- a/bayesian/normalizing_flows/vae.ipynb
+++ b/bayesian/normalizing_flows/vae.ipynb
@@ -2,47 +2,62 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 356,
    "metadata": {},
    "outputs": [],
    "source": [
     "import torch\n",
+    "import torch.distributions as dist\n",
     "from torch import nn\n",
     "from torchvision import datasets, transforms\n",
     "import torch.nn.functional as F\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 366,
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch_size = 128\n",
+    "batch_size = 256\n",
     "dl = torch.utils.data.DataLoader(\n",
     "    datasets.FashionMNIST('.', train=True, transform=transforms.ToTensor(), download=True),\n",
-    "batch_size=batch_size)"
+    "batch_size=batch_size, shuffle=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 367,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f4011231240>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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M3P108XlM0h61bynqs9dX0C0+jzXcz/9r0zLeMy0zrhbcd00uf95E2N+QdJeZfcHMFkn6lqS9DfTxGWa2rHjhRGa2TNLX1L6lqPdK2lZ8vU3SKw32coO2LOPdaZlxNXzfNb78ubvX/iHpQU29In9c0vea6KFDXxsk/aH4eKfp3iS9rKmHdeOaekT0uKQ7JB2Q9F7x+fYW9fYfmlra+y1NBWtNQ719RVNPDd+SdKT4eLDp+y7RVy33G2+XBYLgHXRAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EMRfANHBpxawvfpYAAAAAElFTkSuQmCC\n",
+      "image/png": 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tOW5VhL1f0oJxP39J0sEK2jEhdz9YPB6V9IZG33a0kyNjK+gWj0crbs9fufsRdx929xFJP1WFx65YZnytpJ+7++vF5sqP3UTtatVxqyLs70i62sy+YmbTJH1L0roK2vEFZjar+OBEZjZL0tfVfktRr5P0ePH945J+XWFbPqddlvEuW2ZcFR+7ypc/d/eWf0l6SKOfyL8v6V+qaENJu/5W0v8XXzuqbpukVzV6WfeZRq+I/lnSpZI2StpdPM5to7a9otGlvbdpNFjdFbXtLo2+NdwmaWvx9VDVxy7RrpYcN26XBYLgDjogCMIOBEHYgSAIOxAEYQeCIOxAEIQdCOIvosTy9+Xu/H8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -54,12 +69,18 @@
     }
    ],
    "source": [
-    "plt.imshow(1 - next(iter(dl))[0][2].view(28, 28).numpy(), cmap='Greys')"
+    "batch, _ = next(iter(dl))\n",
+    "\n",
+    "for i in range(3):\n",
+    "    plt.imshow(1 - batch[i].view(28, 28).numpy(), cmap='Greys')\n",
+    "    plt.show()\n",
+    "    \n",
+    "del batch"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 368,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,216 +125,193 @@
     "        mu = z_theta[:, :self.latent_size]\n",
     "        log_var = z_theta[:, self.latent_size:]\n",
     "        z = self.reparameterize(mu, log_var)\n",
-    "        return self.decoder(z), mu, log_var"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# CNN"
+    "        return self.decoder(z), mu, log_var, z"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f40116dc9b0>"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "    \n",
-    "class UnFlatten(nn.Module):\n",
-    "    def forward(self, x):\n",
-    "        # Note -1 dimension is inferred.\n",
-    "        return x.view(x.shape[0], -1, 1, 1)\n",
-    "    \n",
-    "    \n",
-    "class CNNVAE(VAE):\n",
-    "    # TODO: find good architecture. \n",
-    "    def __init__(self, latent_size=400):\n",
-    "        super().__init__(latent_size)\n",
-    "       \n",
-    "        self.encoder = nn.Sequential(\n",
-    "            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Flatten()\n",
-    "        )\n",
-    "        \n",
-    "        self.decoder = nn.Sequential(\n",
-    "            UnFlatten(),\n",
-    "            nn.ConvTranspose2d(self.latent_size, 32, kernel_size=5, stride=2),\n",
-    "            nn.ReLU(),\n",
-    "            nn.ConvTranspose2d(32, 32, kernel_size=4, stride=2),\n",
-    "            nn.ReLU(),\n",
-    "            nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2),\n",
-    "            nn.ReLU(),\n",
-    "            nn.ConvTranspose2d(16, 1, kernel_size=3, stride=1),\n",
-    "            nn.Sigmoid()\n",
-    "        )\n",
-    "\n",
-    "m = CNNVAE()\n",
-    "plt.imshow(m(next(iter(dl))[0])[0][0].view(28, 28).data.numpy(), cmap='Greys')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 369,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def loss_function(reconstruction_x, x, mu, log_var):\n",
-    "    BCE = F.binary_cross_entropy(reconstruction_x, x.view(-1, 784), reduction='sum')\n",
+    "def loss_function(reconstruction_x, x, mu, log_var, z, mc_kl=True, beta=1.):\n",
+    "    likelihood = F.binary_cross_entropy(reconstruction_x, x.view(-1, 784), reduction='sum')\n",
+    "    \n",
+    "    # is worse then the analyitical solution, but is better for comparing ELBO with Flows\n",
+    "    if mc_kl:\n",
+    "        log_prior = dist.Normal(0, 1).log_prob(z)\n",
+    "        log_p_q = dist.Normal(mu, torch.exp(0.5 * log_var)).log_prob(z)\n",
+    "        kl = (log_p_q - log_prior).sum()\n",
+    "    else: \n",
+    "        # see Appendix B from VAE paper:\n",
+    "        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n",
+    "        # https://arxiv.org/abs/1312.6114\n",
+    "        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n",
+    "        kl = -0.5 * torch.sum(1 + log_var - mu**2 - log_var.exp())\n",
     "\n",
-    "    # see Appendix B from VAE paper:\n",
-    "    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n",
-    "    # https://arxiv.org/abs/1312.6114\n",
-    "    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n",
-    "    KLD = -0.5 * torch.sum(1 + log_var - mu**2 - log_var.exp())\n",
-    "\n",
-    "    return BCE + KLD"
+    "    return (likelihood + beta * kl) / x.shape[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 370,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 545.216492\n",
-      "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 304.467712\n",
-      "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 282.475281\n",
-      "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 270.869110\n",
-      "====> Epoch: 1 Average loss: 302.0279\n",
-      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 268.287903\n",
-      "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 273.701294\n",
-      "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 257.615631\n",
-      "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 255.759094\n",
-      "====> Epoch: 2 Average loss: 262.8937\n",
-      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 252.945618\n",
-      "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 264.578156\n",
-      "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 250.188477\n",
-      "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 252.424652\n",
-      "====> Epoch: 3 Average loss: 253.8822\n",
-      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 247.508987\n",
-      "Train Epoch: 4 [19200/60000 (32%)]\tLoss: 260.838867\n",
-      "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 247.394196\n",
-      "Train Epoch: 4 [57600/60000 (96%)]\tLoss: 248.601410\n",
-      "====> Epoch: 4 Average loss: 250.5403\n",
-      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 243.754486\n",
-      "Train Epoch: 5 [19200/60000 (32%)]\tLoss: 258.409210\n",
-      "Train Epoch: 5 [38400/60000 (64%)]\tLoss: 245.839935\n",
-      "Train Epoch: 5 [57600/60000 (96%)]\tLoss: 248.752655\n",
-      "====> Epoch: 5 Average loss: 248.1759\n",
-      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 242.354782\n",
-      "Train Epoch: 6 [19200/60000 (32%)]\tLoss: 256.875854\n",
-      "Train Epoch: 6 [38400/60000 (64%)]\tLoss: 245.116455\n",
-      "Train Epoch: 6 [57600/60000 (96%)]\tLoss: 247.758835\n",
-      "====> Epoch: 6 Average loss: 246.9103\n",
-      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 241.033920\n",
-      "Train Epoch: 7 [19200/60000 (32%)]\tLoss: 256.162842\n",
-      "Train Epoch: 7 [38400/60000 (64%)]\tLoss: 242.574280\n",
-      "Train Epoch: 7 [57600/60000 (96%)]\tLoss: 245.996399\n",
-      "====> Epoch: 7 Average loss: 245.8804\n",
-      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 239.238083\n",
-      "Train Epoch: 8 [19200/60000 (32%)]\tLoss: 255.056564\n",
-      "Train Epoch: 8 [38400/60000 (64%)]\tLoss: 242.117462\n",
-      "Train Epoch: 8 [57600/60000 (96%)]\tLoss: 245.257553\n",
-      "====> Epoch: 8 Average loss: 244.7513\n",
-      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 238.755569\n",
-      "Train Epoch: 9 [19200/60000 (32%)]\tLoss: 253.945724\n",
-      "Train Epoch: 9 [38400/60000 (64%)]\tLoss: 241.528793\n",
-      "Train Epoch: 9 [57600/60000 (96%)]\tLoss: 243.930283\n",
-      "====> Epoch: 9 Average loss: 243.8302\n",
-      "Train Epoch: 10 [0/60000 (0%)]\tLoss: 238.498886\n",
-      "Train Epoch: 10 [19200/60000 (32%)]\tLoss: 253.728821\n",
-      "Train Epoch: 10 [38400/60000 (64%)]\tLoss: 241.584671\n",
-      "Train Epoch: 10 [57600/60000 (96%)]\tLoss: 244.546021\n",
-      "====> Epoch: 10 Average loss: 243.3323\n"
+      "====> Epoch: 1 Average loss: 1.2976\n",
+      "====> Epoch: 2 Average loss: 1.0964\n",
+      "====> Epoch: 3 Average loss: 1.0423\n",
+      "====> Epoch: 4 Average loss: 1.0151\n",
+      "====> Epoch: 5 Average loss: 0.9981\n",
+      "====> Epoch: 6 Average loss: 0.9861\n",
+      "====> Epoch: 7 Average loss: 0.9774\n",
+      "====> Epoch: 8 Average loss: 0.9716\n",
+      "====> Epoch: 9 Average loss: 0.9664\n",
+      "====> Epoch: 10 Average loss: 0.9622\n",
+      "====> Epoch: 11 Average loss: 0.9586\n",
+      "====> Epoch: 12 Average loss: 0.9554\n",
+      "====> Epoch: 13 Average loss: 0.9530\n",
+      "====> Epoch: 14 Average loss: 0.9507\n",
+      "====> Epoch: 15 Average loss: 0.9489\n",
+      "====> Epoch: 16 Average loss: 0.9474\n",
+      "====> Epoch: 17 Average loss: 0.9460\n",
+      "====> Epoch: 18 Average loss: 0.9447\n",
+      "====> Epoch: 19 Average loss: 0.9438\n",
+      "====> Epoch: 20 Average loss: 0.9429\n",
+      "====> Epoch: 21 Average loss: 0.9417\n",
+      "====> Epoch: 22 Average loss: 0.9409\n",
+      "====> Epoch: 23 Average loss: 0.9403\n",
+      "====> Epoch: 24 Average loss: 0.9393\n",
+      "====> Epoch: 25 Average loss: 0.9386\n",
+      "====> Epoch: 26 Average loss: 0.9379\n",
+      "====> Epoch: 27 Average loss: 0.9372\n",
+      "====> Epoch: 28 Average loss: 0.9367\n",
+      "====> Epoch: 29 Average loss: 0.9359\n",
+      "====> Epoch: 30 Average loss: 0.9358\n",
+      "====> Epoch: 31 Average loss: 0.9351\n",
+      "====> Epoch: 32 Average loss: 0.9343\n",
+      "====> Epoch: 33 Average loss: 0.9337\n",
+      "====> Epoch: 34 Average loss: 0.9335\n",
+      "====> Epoch: 35 Average loss: 0.9331\n",
+      "====> Epoch: 36 Average loss: 0.9328\n",
+      "====> Epoch: 37 Average loss: 0.9322\n",
+      "====> Epoch: 38 Average loss: 0.9316\n",
+      "====> Epoch: 39 Average loss: 0.9312\n",
+      "====> Epoch: 40 Average loss: 0.9308\n",
+      "====> Epoch: 41 Average loss: 0.9305\n",
+      "====> Epoch: 42 Average loss: 0.9301\n",
+      "====> Epoch: 43 Average loss: 0.9297\n",
+      "====> Epoch: 44 Average loss: 0.9293\n",
+      "====> Epoch: 45 Average loss: 0.9290\n",
+      "====> Epoch: 46 Average loss: 0.9288\n",
+      "====> Epoch: 47 Average loss: 0.9284\n",
+      "====> Epoch: 48 Average loss: 0.9280\n",
+      "====> Epoch: 49 Average loss: 0.9279\n",
+      "====> Epoch: 50 Average loss: 0.9275\n",
+      "====> Epoch: 51 Average loss: 0.9274\n",
+      "====> Epoch: 52 Average loss: 0.9271\n",
+      "====> Epoch: 53 Average loss: 0.9268\n",
+      "====> Epoch: 54 Average loss: 0.9263\n",
+      "====> Epoch: 55 Average loss: 0.9262\n",
+      "====> Epoch: 56 Average loss: 0.9260\n",
+      "====> Epoch: 57 Average loss: 0.9260\n",
+      "====> Epoch: 58 Average loss: 0.9258\n",
+      "====> Epoch: 59 Average loss: 0.9255\n",
+      "====> Epoch: 60 Average loss: 0.9253\n",
+      "====> Epoch: 61 Average loss: 0.9252\n",
+      "====> Epoch: 62 Average loss: 0.9247\n",
+      "====> Epoch: 63 Average loss: 0.9246\n",
+      "====> Epoch: 64 Average loss: 0.9245\n",
+      "====> Epoch: 65 Average loss: 0.9245\n",
+      "====> Epoch: 66 Average loss: 0.9242\n",
+      "====> Epoch: 67 Average loss: 0.9239\n",
+      "====> Epoch: 68 Average loss: 0.9239\n",
+      "====> Epoch: 69 Average loss: 0.9236\n",
+      "====> Epoch: 70 Average loss: 0.9236\n",
+      "====> Epoch: 71 Average loss: 0.9233\n",
+      "====> Epoch: 72 Average loss: 0.9232\n",
+      "====> Epoch: 73 Average loss: 0.9230\n",
+      "====> Epoch: 74 Average loss: 0.9229\n",
+      "====> Epoch: 75 Average loss: 0.9227\n",
+      "====> Epoch: 76 Average loss: 0.9226\n",
+      "====> Epoch: 77 Average loss: 0.9227\n",
+      "====> Epoch: 78 Average loss: 0.9227\n",
+      "====> Epoch: 79 Average loss: 0.9222\n",
+      "====> Epoch: 80 Average loss: 0.9223\n",
+      "====> Epoch: 81 Average loss: 0.9220\n",
+      "====> Epoch: 82 Average loss: 0.9219\n",
+      "====> Epoch: 83 Average loss: 0.9217\n",
+      "====> Epoch: 84 Average loss: 0.9218\n",
+      "====> Epoch: 85 Average loss: 0.9214\n",
+      "====> Epoch: 86 Average loss: 0.9213\n",
+      "====> Epoch: 87 Average loss: 0.9212\n",
+      "====> Epoch: 88 Average loss: 0.9215\n",
+      "====> Epoch: 89 Average loss: 0.9212\n",
+      "====> Epoch: 90 Average loss: 0.9211\n",
+      "====> Epoch: 91 Average loss: 0.9208\n",
+      "====> Epoch: 92 Average loss: 0.9210\n",
+      "====> Epoch: 93 Average loss: 0.9207\n",
+      "====> Epoch: 94 Average loss: 0.9204\n",
+      "====> Epoch: 95 Average loss: 0.9203\n",
+      "====> Epoch: 96 Average loss: 0.9205\n",
+      "====> Epoch: 97 Average loss: 0.9203\n",
+      "====> Epoch: 98 Average loss: 0.9204\n",
+      "====> Epoch: 99 Average loss: 0.9202\n",
+      "====> Epoch: 100 Average loss: 0.9198\n"
      ]
     }
    ],
    "source": [
-    "latent_size = 250\n",
+    "latent_size = 64\n",
     "m = VAE(latent_size)\n",
     "m.cuda()\n",
     "\n",
-    "def train(epoch, m, device, optimizer):\n",
+    "vae_loss = []\n",
+    "\n",
+    "def log_training(batch_idx, x, loss):\n",
+    "    if batch_idx % 400 == 0:\n",
+    "        print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
+    "            epoch, batch_idx * len(x), len(dl.dataset),\n",
+    "            100. * batch_idx / len(dl),\n",
+    "            loss.item() / len(x)))\n",
+    "\n",
+    "def train(epoch, m, device, optimizer, beta):\n",
     "    m.train()\n",
     "    train_loss = 0\n",
     "    for batch_idx, (x, _) in enumerate(dl):\n",
     "        x = x.to(device).view(-1, 784)\n",
     "        \n",
     "        optim.zero_grad()\n",
-    "        recon_batch, mu, logvar = m(x)\n",
-    "        loss = loss_function(recon_batch, x, mu, logvar)\n",
+    "        reconstruction_x, mu, log_var, z = m(x)\n",
+    "        loss = loss_function(reconstruction_x, x, mu, log_var, z, beta)\n",
     "        loss.backward()\n",
     "        \n",
     "        train_loss += loss.item()\n",
     "        optimizer.step()\n",
-    "        if batch_idx % 150 == 0:\n",
-    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
-    "                epoch, batch_idx * len(x), len(dl.dataset),\n",
-    "                100. * batch_idx / len(dl),\n",
-    "                loss.item() / len(x)))\n",
-    "\n",
-    "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(dl.dataset)))\n",
+    "    \n",
+    "    avg_loss = train_loss / len(dl.dataset)\n",
+    "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, avg_loss))\n",
+    "    vae_loss.append(avg_loss)\n",
     "\n",
     "\n",
-    "optim = torch.optim.Adam(m.parameters(), 1e-3)\n",
+    "optim = torch.optim.Adam(m.parameters(), 5e-4)\n",
     "\n",
-    "for epoch in range(1, 11):\n",
-    "    train(epoch, m, 'cuda', optim)"
+    "for epoch in range(1, 101):\n",
+    "    beta = 1 / 90 * epoch\n",
+    "    train(epoch, m, 'cuda', optim, beta)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with torch.no_grad():\n",
-    "    # take 5 samples\n",
-    "    sample = torch.randn(5, latent_size).to('cuda')\n",
-    "    sample = m.decoder(sample).cpu().view(-1, 28, 28)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 371,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -325,7 +323,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -337,7 +335,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -349,7 +347,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -361,7 +359,127 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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09cz/s5mOc/c17j7m7mPRnmQAemtWYTezszUV9N+4+x8kyd33uvspdz8t6VeSbujdMAE0NZtX403So5K2uPvPp92+ZNqXfUfSpu4PD0C3zObV+JslfV/Sa2a2sbrtJ5LuMbNVklzShKQf9GSEALpiNq/G/1nSTJNzn+3+cAD0ClfQAYUg7EAhCDtQCMIOFIKwA4Ug7EAhCDtQCMIOFIKwA4Ug7EAhCDtQCMIOFIKwA4Ug7EAhzN37dzKzSUnbp900LGl/3wbwxQzq2AZ1XBJj61Q3x/Y1d59x/be+hv1zJzcbd/ex1gYQGNSxDeq4JMbWqX6NjT/jgUIQdqAQbYd9Tcvnjwzq2AZ1XBJj61Rfxtbq/9kB9E/bz+wA+oSwA4VoJexmdruZvWlmb5vZQ22MoY6ZTZjZa9U21OMtj+UxM9tnZpum3TZkZs+Z2VvV+xn32GtpbAOxjXewzXirj13b25/3/f/sZjZX0lZJfydpp6RXJN3j7q/3dSA1zGxC0pi7t34Bhpl9S9IHkv7D3a+pbvtnSQfd/ZHqH8qL3P0fB2RsD0v6oO1tvKvdipZM32Zc0t2S/kEtPnbBuP5efXjc2nhmv0HS2+6+zd1PSPqtpLtaGMfAc/d1kg6ecfNdktZWH6/V1C9L39WMbSC4+x5331B9fFTSp9uMt/rYBePqizbCvkzSjmmf79Rg7ffukv5oZuvNbHXbg5nBYnffI0398ki6pOXxnCndxrufzthmfGAeu062P2+qjbDPtJXUIPX/bnb3b0q6Q9ID1Z+rmJ1ZbePdLzNsMz4QOt3+vKk2wr5T0oppny+XtLuFcczI3XdX7/dJelKDtxX13k930K3e72t5PP9vkLbxnmmbcQ3AY9fm9udthP0VSSvN7HIzmyfpe5KebmEcn2NmC6oXTmRmCyTdpsHbivppSfdVH98n6akWx/IZg7KNd90242r5sWt9+3N37/ubpDs19Yr8O5L+qY0x1IzrryT9b/W2ue2xSXpcU3/WfaKpv4jul3SxpOclvVW9Hxqgsf2npNckvaqpYC1paWx/o6n/Gr4qaWP1dmfbj10wrr48blwuCxSCK+iAQhB2oBCEHSgEYQcKQdiBQhB2oBCEHSjE/wFZ0l4Po5MCdQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -373,23 +491,44 @@
     }
    ],
    "source": [
-    "for i in range(sample.shape[0]):\n",
-    "    plt.imshow(sample[i].view(28, 28), cmap='Greys')\n",
-    "    plt.show()"
+    "def show_samples(m, n=15, device='cuda'):\n",
+    "    with torch.no_grad():\n",
+    "        # take n samples\n",
+    "        sample = torch.randn(n, latent_size).to(device)\n",
+    "        sample = m.decoder(sample).cpu().view(-1, 28, 28)\n",
+    "        \n",
+    "    for i in range(sample.shape[0]):\n",
+    "        plt.imshow(sample[i].view(28, 28), cmap='Greys')\n",
+    "        plt.show()\n",
+    "        \n",
+    "show_samples(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic planar flow"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 372,
    "metadata": {},
    "outputs": [],
    "source": [
     "class PlanarFlow(nn.Module):\n",
     "    def __init__(self, size=1, init_sigma=0.01):\n",
+    "        \"\"\"\n",
+    "        shape u = (batch_size, z_size, 1)\n",
+    "        shape w = (batch_size, 1, z_size)\n",
+    "        shape b = (batch_size, 1, 1)\n",
+    "        shape z = (batch_size, z_size).\n",
+    "        \"\"\"\n",
     "        super().__init__()\n",
-    "        self.u = nn.Parameter(torch.randn(size, ).normal_(0, 0.01))\n",
-    "        self.w = nn.Parameter(torch.randn(size, ).normal_(0, 0.01))\n",
-    "        self.b = nn.Parameter(torch.zeros(size, 1))\n",
+    "        self.u = nn.Parameter(torch.randn(size, 1).normal_(0, 0.01))\n",
+    "        self.w = nn.Parameter(torch.randn(1, size).normal_(0, 0.01))\n",
+    "        self.b = nn.Parameter(torch.zeros(1))\n",
     "    \n",
     "    @property\n",
     "    def normalized_u(self):\n",
@@ -400,10 +539,12 @@
     "        Rezende et al. Variational Inference with Normalizing Flows\n",
     "        https://arxiv.org/pdf/1505.05770.pdf\n",
     "        \"\"\"\n",
+    "        # softplus\n",
     "        def m(x):\n",
     "            return -1 + torch.log(1 + torch.exp(x))\n",
-    "        wtu = self.w @ self.u.t()\n",
-    "        return self.u + (m(wtu) - wtu) * self.w / (self.w @ self.w.t())\n",
+    "        wtu = self.w @ self.u\n",
+    "        w_div_w2 = self.w.t() / torch.sum(self.w ** 2, dim=1, keepdim=True)\n",
+    "        return self.u + (m(wtu) - wtu) * w_div_w2\n",
     "    \n",
     "    def psi(self, z):\n",
     "        \"\"\"\n",
@@ -413,7 +554,7 @@
     "        Rezende et al. Variational Inference with Normalizing Flows\n",
     "        https://arxiv.org/pdf/1505.05770.pdf\n",
     "        \"\"\"\n",
-    "        return (self.h_prime(z @ self.w.t() + self.b).t() @ self.w.t())\n",
+    "        return self.h_prime(z @ self.w.t() + self.b) @ self.w\n",
     "    \n",
     "    def h(self, x):\n",
     "        return torch.tanh(x)\n",
@@ -423,23 +564,763 @@
     "        \n",
     "    def forward(self, z):\n",
     "        if isinstance(z, tuple):\n",
-    "            z, accumulating_log_abs_det = z\n",
+    "            z, accumulating_ldj = z\n",
     "        else:\n",
-    "            z, accumulating_log_abs_det = z, 0\n",
-    "        \n",
+    "            z, accumulating_ldj = z, 0\n",
     "        psi = self.psi(z)\n",
+    "\n",
     "        u = self.normalized_u\n",
-    "        print(psi.shape, u.shape)\n",
+    "\n",
     "        # determinant of jacobian\n",
-    "        det = (1 + psi @ u.t()).unsqueeze(1)\n",
-    "        \n",
+    "        det = (1 + psi @ u)\n",
+    "\n",
     "        # log |det Jac|\n",
-    "        log_abs_det = torch.log(torch.abs(det) + 1e-6)\n",
+    "        ldj = torch.log(torch.abs(det) + 1e-6)\n",
     "        \n",
-    "        fz = z + u + self.h((z @ self.w.t()).unsqueeze(1) + self.b)\n",
-    "        print(fz.shape)\n",
+    "        wzb = z @ self.w.t() + self.b\n",
+    " \n",
+    "        fz = z + (u.t() * self.h(wzb))\n",
+    "\n",
+    "        return fz, ldj + accumulating_ldj\n",
+    "\n",
+    "\n",
+    "\n",
+    "class FlowVAE(VAE):\n",
+    "    def __init__(self, latent_size, n_flows=20):\n",
+    "        super().__init__(latent_size)\n",
+    "                \n",
+    "        self.flow = nn.Sequential(*[\n",
+    "            PlanarFlow(latent_size) for _ in range(n_flows)\n",
+    "        ])   \n",
+    "            \n",
+    "    def forward(self, x):\n",
+    "        z_theta = self.encoder(x)\n",
+    "        mu0 = z_theta[:, :self.latent_size]\n",
+    "        log_var0 = z_theta[:, self.latent_size:]\n",
+    "        z0 = self.reparameterize(mu0, log_var0)\n",
+    "        zk, accumulating_ldj = self.flow(z0)\n",
     "        \n",
-    "        return fz, log_abs_det + accumulating_log_abs_det\n",
+    "        return z0, zk, self.decoder(zk), accumulating_ldj, mu0, log_var0\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def det_loss(reconstruction_x, x, mu, log_var, z_0, z_k, ldj, beta=1.):\n",
+    "    \"\"\"\n",
+    "    :param z_mu: mean of z_0\n",
+    "    :param z_var: variance of z_0\n",
+    "    :param z_0: first stochastic latent variable\n",
+    "    :param z_k: last stochastic latent variable\n",
+    "    :param ldj: log det jacobian\n",
+    "    \"\"\"\n",
+    "\n",
+    "    batch_size = x.size(0)\n",
+    "\n",
+    "    # - N E_q0 [ ln p(x|z_k) ]\n",
+    "    likelihood = F.binary_cross_entropy(reconstruction_x, x, reduction='sum')\n",
+    "\n",
+    "    # ln p(z_k)  (not averaged)\n",
+    "    log_p_zk = dist.Normal(0, 1).log_prob(z_k)\n",
+    "    # ln q(z_0)  (not averaged)\n",
+    "    log_q_z0 = dist.Normal(mu, torch.exp(0.5 * log_var)).log_prob(z_0)\n",
+    "    \n",
+    "    # N E_q0[ ln q(z_0) - ln p(z_k) ]\n",
+    "    summed_logs = torch.sum(log_q_z0 - log_p_zk)\n",
+    "\n",
+    "    # sum over batches\n",
+    "    summed_ldj = torch.sum(ldj)\n",
+    "\n",
+    "    # ldj = N E_q_z0[\\sum_k log |det dz_k/dz_k-1| ]\n",
+    "    kl = (summed_logs - summed_ldj)\n",
+    "    loss = likelihood + beta * kl\n",
+    "\n",
+    "    return loss / x.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(-41.4189)"
+      ]
+     },
+     "execution_count": 385,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = dist.Normal(0, 1)\n",
+    "a.log_prob(9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====> Epoch: 1 Average loss: 1.1884\n",
+      "====> Epoch: 2 Average loss: 0.9730\n",
+      "====> Epoch: 3 Average loss: 0.9385\n",
+      "====> Epoch: 4 Average loss: 0.9209\n",
+      "====> Epoch: 5 Average loss: 0.9114\n",
+      "====> Epoch: 6 Average loss: 0.9047\n",
+      "====> Epoch: 7 Average loss: 0.8987\n",
+      "====> Epoch: 8 Average loss: 0.8945\n",
+      "====> Epoch: 9 Average loss: 0.8904\n",
+      "====> Epoch: 10 Average loss: 0.8878\n",
+      "====> Epoch: 11 Average loss: 0.8851\n",
+      "====> Epoch: 12 Average loss: 0.8833\n",
+      "====> Epoch: 13 Average loss: 0.8820\n",
+      "====> Epoch: 14 Average loss: 0.8804\n",
+      "====> Epoch: 15 Average loss: 0.8794\n",
+      "====> Epoch: 16 Average loss: 0.8789\n",
+      "====> Epoch: 17 Average loss: 0.8782\n",
+      "====> Epoch: 18 Average loss: 0.8776\n",
+      "====> Epoch: 19 Average loss: 0.8775\n",
+      "====> Epoch: 20 Average loss: 0.8774\n",
+      "====> Epoch: 21 Average loss: 0.8776\n",
+      "====> Epoch: 22 Average loss: 0.8776\n",
+      "====> Epoch: 23 Average loss: 0.8781\n",
+      "====> Epoch: 24 Average loss: 0.8782\n",
+      "====> Epoch: 25 Average loss: 0.8784\n",
+      "====> Epoch: 26 Average loss: 0.8791\n",
+      "====> Epoch: 27 Average loss: 0.8794\n",
+      "====> Epoch: 28 Average loss: 0.8798\n",
+      "====> Epoch: 29 Average loss: 0.8805\n",
+      "====> Epoch: 30 Average loss: 0.8811\n",
+      "====> Epoch: 31 Average loss: 0.8818\n",
+      "====> Epoch: 32 Average loss: 0.8826\n",
+      "====> Epoch: 33 Average loss: 0.8832\n",
+      "====> Epoch: 34 Average loss: 0.8840\n",
+      "====> Epoch: 35 Average loss: 0.8842\n",
+      "====> Epoch: 36 Average loss: 0.8853\n",
+      "====> Epoch: 37 Average loss: 0.8860\n",
+      "====> Epoch: 38 Average loss: 0.8866\n",
+      "====> Epoch: 39 Average loss: 0.8876\n",
+      "====> Epoch: 40 Average loss: 0.8884\n",
+      "====> Epoch: 41 Average loss: 0.8889\n",
+      "====> Epoch: 42 Average loss: 0.8896\n",
+      "====> Epoch: 43 Average loss: 0.8904\n",
+      "====> Epoch: 44 Average loss: 0.8913\n",
+      "====> Epoch: 45 Average loss: 0.8920\n",
+      "====> Epoch: 46 Average loss: 0.8927\n",
+      "====> Epoch: 47 Average loss: 0.8939\n",
+      "====> Epoch: 48 Average loss: 0.8942\n",
+      "====> Epoch: 49 Average loss: 0.8953\n",
+      "====> Epoch: 50 Average loss: 0.8958\n",
+      "====> Epoch: 51 Average loss: 0.8966\n",
+      "====> Epoch: 52 Average loss: 0.8973\n",
+      "====> Epoch: 53 Average loss: 0.8980\n",
+      "====> Epoch: 54 Average loss: 0.8989\n",
+      "====> Epoch: 55 Average loss: 0.8994\n",
+      "====> Epoch: 56 Average loss: 0.9003\n",
+      "====> Epoch: 57 Average loss: 0.9010\n",
+      "====> Epoch: 58 Average loss: 0.9018\n",
+      "====> Epoch: 59 Average loss: 0.9025\n",
+      "====> Epoch: 60 Average loss: 0.9032\n",
+      "====> Epoch: 61 Average loss: 0.9039\n",
+      "====> Epoch: 62 Average loss: 0.9046\n",
+      "====> Epoch: 63 Average loss: 0.9055\n",
+      "====> Epoch: 64 Average loss: 0.9060\n",
+      "====> Epoch: 65 Average loss: 0.9067\n",
+      "====> Epoch: 66 Average loss: 0.9075\n",
+      "====> Epoch: 67 Average loss: 0.9080\n",
+      "====> Epoch: 68 Average loss: 0.9088\n",
+      "====> Epoch: 69 Average loss: 0.9096\n",
+      "====> Epoch: 70 Average loss: 0.9104\n",
+      "====> Epoch: 71 Average loss: 0.9108\n",
+      "====> Epoch: 72 Average loss: 0.9116\n",
+      "====> Epoch: 73 Average loss: 0.9122\n",
+      "====> Epoch: 74 Average loss: 0.9132\n",
+      "====> Epoch: 75 Average loss: 0.9136\n",
+      "====> Epoch: 76 Average loss: 0.9142\n",
+      "====> Epoch: 77 Average loss: 0.9148\n",
+      "====> Epoch: 78 Average loss: 0.9157\n",
+      "====> Epoch: 79 Average loss: 0.9162\n",
+      "====> Epoch: 80 Average loss: 0.9169\n",
+      "====> Epoch: 81 Average loss: 0.9175\n",
+      "====> Epoch: 82 Average loss: 0.9183\n",
+      "====> Epoch: 83 Average loss: 0.9188\n",
+      "====> Epoch: 84 Average loss: 0.9197\n",
+      "====> Epoch: 85 Average loss: 0.9201\n",
+      "====> Epoch: 86 Average loss: 0.9206\n",
+      "====> Epoch: 87 Average loss: 0.9213\n",
+      "====> Epoch: 88 Average loss: 0.9219\n",
+      "====> Epoch: 89 Average loss: 0.9227\n",
+      "====> Epoch: 90 Average loss: 0.9232\n",
+      "====> Epoch: 91 Average loss: 0.9239\n",
+      "====> Epoch: 92 Average loss: 0.9242\n",
+      "====> Epoch: 93 Average loss: 0.9250\n",
+      "====> Epoch: 94 Average loss: 0.9255\n",
+      "====> Epoch: 95 Average loss: 0.9263\n",
+      "====> Epoch: 96 Average loss: 0.9267\n",
+      "====> Epoch: 97 Average loss: 0.9273\n",
+      "====> Epoch: 98 Average loss: 0.9279\n",
+      "====> Epoch: 99 Average loss: 0.9285\n",
+      "====> Epoch: 100 Average loss: 0.9291\n"
+     ]
+    }
+   ],
+   "source": [
+    "latent_size = 64\n",
+    "m = FlowVAE(latent_size, n_flows=16)\n",
+    "m.cuda()\n",
+    "\n",
+    "flow_loss = []\n",
+    "\n",
+    "def train(epoch, m, device, optimizer, beta):\n",
+    "    m.train()\n",
+    "    train_loss = 0\n",
+    "    for batch_idx, (x, _) in enumerate(dl):\n",
+    "        x = x.to(device).view(-1, 784)\n",
+    "        \n",
+    "        optim.zero_grad()\n",
+    "        z0, zk, reconstruction_x, accumulating_log_abs_det, mu0, log_var0 = m(x)\n",
+    "    \n",
+    "        loss = det_loss(x=x, \n",
+    "                        reconstruction_x=reconstruction_x, \n",
+    "                        mu=mu0,\n",
+    "                        log_var=log_var0,\n",
+    "                        ldj=accumulating_log_abs_det,\n",
+    "                        z_0=z0,\n",
+    "                        z_k=zk,\n",
+    "                        beta=beta\n",
+    "                        )\n",
+    "        loss.backward()\n",
+    "        \n",
+    "        train_loss += loss.item()\n",
+    "        optimizer.step()\n",
+    "    \n",
+    "    avg_loss = train_loss / len(dl.dataset)\n",
+    "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, avg_loss))\n",
+    "    flow_loss.append(avg_loss)\n",
+    "\n",
+    "optim = torch.optim.Adam(m.parameters(), 5e-4)\n",
+    "\n",
+    "for epoch in range(1, 101):\n",
+    "    beta = 1 / 90 * epoch\n",
+    "    train(epoch, m, 'cuda', optim, beta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pue57ku41s5skuaSDkr41lB4CGIjlfBr/W0lLDRzePfjuABgWzqADiiDsQBGEHSiCsANFEHagCMIOFEHYgSIIO1AEYQeKIOxAEYQdKIKwA0UQdqAIwg4UYe4+up2ZzUn630VXTUg6NrIOfDrj2rdx7ZdE3/o1yL79mbsvOf/bSMP+iZ2bTbv7VGcdCIxr38a1XxJ969eo+sa/8UARhB0oouuw7+x4/5Fx7du49kuib/0aSd86fc8OYHS6fmUHMCKEHSiik7Cb2R1m9t9m9pqZPdxFH3oxs4NmtrdZhnq64748bmZHzWzfous2mdkzZvZqc7nkGnsd9W0slvEOlhnv9Nh1vfz5yN+zm9lKSf8j6W8kHZb0vKR73f2/RtqRHszsoKQpd+/8BAwz+5KkdyT9q7vf0Fz3D5KOu/ujzR/Kje7+d2PSt0ckvdP1Mt7NakVbFy8zLuluSd9Uh8cu6NffagTHrYtX9pslvebub7j7GUk/l7S9g36MPXd/VtLx867eLmlX8/0uLTxZRq5H38aCu8+4+4vN96clfbzMeKfHLujXSHQR9isl/W7Rz4c1Xuu9u6Rfm9kLZraj684s4Qp3n5EWnjySLu+4P+dLl/EepfOWGR+bY9fP8udtdRH2pZaSGqf6363u/kVJd0p6oPl3FcuzrGW8R2WJZcbHQr/Ln7fVRdgPS9q26OfPSDrSQT+W5O5Hmsujkp7U+C1FPfvxCrrN5dGO+/P/xmkZ76WWGdcYHLsulz/vIuzPS7rGzD5rZqslfV3S0x304xPMbE3zwYnMbI2kr2j8lqJ+WtJ9zff3SXqqw778gXFZxrvXMuPq+Nh1vvy5u4/8S9JdWvhE/nVJf99FH3r0688lvdx87e+6b5Ke0MK/dR9p4T+i+yVdJmmPpFeby01j1Ld/k7RX0itaCNbWjvr2V1p4a/iKpJear7u6PnZBv0Zy3DhdFiiCM+iAIgg7UARhB4og7EARhB0ogrADRRB2oIj/AxURYM49iauXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZLZJc0oikH3ZkhNOUtWEuu+yySsdH9WxZ4dtvvz2sZ+2rrH129dVXN61dd9114bFZi6ivb8qXYv4ma1lGLa6qU3uzluf777/ftDY8PBwem/1Mly5dGtZvu+22sF6H6bwa/1dJU/3EeranDuCruIMOKARhBwpB2IFCEHagEIQdKARhBwpxXi0lHcm2933iiSfC+sqVK8N6tBR11ie/7777wnp2G3G0VLQU94SzHn2V5bnbcXwV2XbT0TTUqtOKs/sysj59HeeNKztQCMIOFIKwA4Ug7EAhCDtQCMIOFIKwA4WwrFfZ1iczG5P00aSHlkja37UBfD29OrZeHZfE2FrVzrH9nbtPeeNGV8P+lSc3G3L3wdoGEOjVsfXquCTG1qpujY1f44FCEHagEHWHfU3Nzx/p1bH16rgkxtaqroyt1v+zA+ieuq/sALqEsAOFqCXsZna3mb1vZtvN7Mk6xtCMmY2Y2bCZbTKzoZrH8pyZ7TOzLZMeW2xmr5nZh4238cLu3R3b02a2u3HuNpnZvTWNbbmZ/cXMtpnZe2b2o8bjtZ67YFxdOW9d/z+7mc2Q9IGkf5K0S9IGSQ+5+9auDqQJMxuRNOjutd+AYWbfknRM0m/d/abGY/8u6YC7P9P4h7LP3f+1R8b2tKRjdW/j3ditaGDyNuOSHpD0z6rx3AXjelBdOG91XNlXSdru7jvc/XNJv5d0fw3j6Hnu/rqkA+c8fL+ktY3312riL0vXNRlbT3D3UXd/u/H+UUlfbDNe67kLxtUVdYR9maSPJ328S72137tL+rOZbTSz1XUPZgpXuPuoNPGXR9LlNY/nXOk23t10zjbjPXPuWtn+vKo6wj7V4lu91P+7w92/KekeSY82fl3F9ExrG+9umWKb8Z7Q6vbnVdUR9l2Slk/6+CpJe2oYx5TcfU/j7T5JL6r3tqLe+8UOuo23+2oez9/00jbeU20zrh44d3Vuf15H2DdIut7MvmFmsyV9X9LLNYzjK8xsfuOFE5nZfEnfUe9tRf2ypIcb7z8s6aUax/IlvbKNd7NtxlXzuat9+3N37/ofSfdq4hX5/5P0b3WMocm4rpH0buPPe3WPTdLzmvi17pQmfiN6RNJlktZJ+rDxdnEPje2/JQ1L2qyJYA3UNLZ/0MR/DTdL2tT4c2/d5y4YV1fOG7fLAoXgDjqgEIQdKARhBwpB2IFCEHagEIQdKARhBwrx/6fkng/WREKyAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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7iP3UzP6tuuGJSKvMZn32MQBjxfeTJA8BWFv1wESktf6q9+wkBwB8GcDe4qYXSR4guZPk8gbbbCc5RHJoYmJipruISBvMOtlJdgP4HYAfmNlFAD8H8CUAGzF15v/xTNuZ2Q4zGzSzwb6+vhYMWUSaMatkJ7kAU4n+azP7PQCY2Skzu2VmtwH8AsCm6oYpImWFyc6p9pzXABwys59Mu71/2t2+DuBg64cnIq0ym0/jnwTwbQAfkNxf3PYygK0kNwIwAMMAvlfJCKfxWh6jNtCoXTL6PMGbEjnadzSVdDRNdVTa80pMUVkvmsbaW/YYAE6dOuXGvZLopUuX3G2jsl40DbZXuov2Hf1OTp8+7cZXrlzpxqOyYhVm82n8nwDM1HxbqqYuIu2lK+hEklCyiyShZBdJQskukoSSXSQJJbtIEh01lXQ0va5Xd+3t7XW33bx5sxvftMm/ANCrN0c1/Gg56ajNNJru2Xv8aN9Rq+b8+f5LpEyrZvT7jvYdHXcvHh3zqA4eHdfoGgFv+6qmmdaZXSQJJbtIEkp2kSSU7CJJKNlFklCyiyShZBdJgu2c0pbkBIBj025aAcBvDK5Pp46tU8cFaGzNauXYHjSzGed/a2uyf2Hn5JCZDdY2AEenjq1TxwVobM1q19j0Z7xIEkp2kSTqTvYdNe/f06lj69RxARpbs9oytlrfs4tI+9R9ZheRNlGyiyRRS7KTfJrkRyQ/JvlSHWNohOQwyQ9I7ic5VPNYdpIcJ3lw2m29JN8mebj4OuMaezWN7RWSJ4tjt5/kszWNbT3JP5I8RPJDkt8vbq/12Dnjastxa/t7dpLzAPwfgH8CMALgXQBbzex/2zqQBkgOAxg0s9ovwCD5VQCXAPzKzP62uO1fAZw1s1eL/yiXm9k/d8jYXgFwqe5lvIvVivqnLzMO4DkA30GNx84Z1zfRhuNWx5l9E4CPzeyomV0H8BsAW2oYR8czs3cAnL3j5i0AdhXf78LUi6XtGoytI5jZmJntK76fBPD5MuO1HjtnXG1RR7KvBXBi2s8j6Kz13g3AH0i+R3J73YOZwSozGwOmXjwA/HWG2i9cxrud7lhmvGOOXTPLn5dVR7LPNMFWJ9X/njSzrwB4BsALxZ+rMjuzWsa7XWZYZrwjNLv8eVl1JPsIgPXTfl4HYLSGcczIzEaLr+MA3kDnLUV96vMVdIuv4zWP5886aRnvmZYZRwccuzqXP68j2d8F8DDJDSQXAvgWgN01jOMLSHYVH5yAZBeAzei8pah3A9hWfL8NwJs1juUvdMoy3o2WGUfNx6725c/NrO3/ADyLqU/kjwD4lzrG0GBcDwF4v/j3Yd1jA/A6pv6su4Gpv4ieB3A/gD0ADhdfeztobP8J4AMABzCVWP01je0fMPXW8ACA/cW/Z+s+ds642nLcdLmsSBK6gk4kCSW7SBJKdpEklOwiSSjZRZJQsoskoWQXSeL/AZds3NwHep6PAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_samples(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=101x128 at 0x7F981CEEC5C0>"
+      ]
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = Image.open('/home/ritchie46/Downloads/Donald_Trump_official_portrait.jpg')\n",
+    "img.thumbnail((128, 128))\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = img.convert('1')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f98508e2198>"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = 1 - np.array(img)\n",
+    "plt.imshow(img, cmap=\"Greys\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img.shape\n",
+    "img = torch.tensor(img, dtype=torch.float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_z(z):\n",
+    "    a = z.numpy().reshape(img.shape)\n",
+    "    plt.imshow(a, cmap=\"Greys\")\n",
+    "    plt.show()\n",
+    "    \n",
+    "size = img.shape[0] * img.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class Flow(nn.Module):\n",
+    "    def __init__(self, n_flows=10):\n",
+    "        super().__init__()\n",
+    "        self.flow =  nn.Sequential(*[\n",
+    "            PlanarFlow(size) for _ in range(n_flows)\n",
+    "        ])\n",
+    "        self.mu = nn.Parameter(torch.randn(size,).normal_(0, 0.01))\n",
+    "        self.log_var = nn.Parameter(torch.randn(size,).normal_(0, 0.01))\n",
+    "        \n",
+    "    def forward(self):\n",
+    "        std = torch.exp(0.5 * self.log_var)\n",
+    "        eps = torch.randn_like(std)  # unit gaussian\n",
+    "        z0 = self.mu + eps * std \n",
+    "        zk, ldj = self.flow(z)\n",
+    "        return z0, zk, ldj, self.mu, self.log_var\n",
+    "        \n",
+    "\n",
+    "flow = Flow()\n",
+    "z0, zk, ldj, mu, log_var = flow()\n",
+    "\n",
+    "show_z(z0.data)\n",
+    "show_z(zk.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flow = Flow()\n",
+    "\n",
+    "def train_flow(flow, epochs=250):\n",
+    "    optim = torch.optim.Adam(flow.parameters(), lr=1e-3)\n",
+    "    for _ in range(epochs):\n",
+    "\n",
+    "        z0, zk, ldj, mu, log_var = flow()\n",
+    "\n",
+    "        loss = det_loss(reconstruction_x=torch.sigmoid(zk),\n",
+    "                       x=img,\n",
+    "                       mu=mu,\n",
+    "                       log_var=log_var,\n",
+    "                       z_0=z0,\n",
+    "                       z_k=zk,\n",
+    "                       ldj=ldj)\n",
+    "        loss.backward()\n",
+    "        optim.step()\n",
+    "        optim.zero_grad()  \n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Using a target size (torch.Size([128, 101])) that is different to the input size (torch.Size([1, 12928])) is deprecated. Please ensure they have the same size.\n",
+      "  del sys.path[0]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N flows: 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N flows: 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N flows: 10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N flows: 20\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N flows: 32\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for n in [2, 5, 10, 20, 32]:\n",
+    "    flow = Flow(n)\n",
+    "    train_flow(flow)\n",
+    "    z0, zk, ldj, mu, log_var = flow()\n",
+    "    print('N flows:', n)\n",
+    "    show_z(torch.sigmoid(zk).data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amortized planar flow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 387,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://github.com/riannevdberg/sylvester-flows\n",
     "\n",
     "class Planar(nn.Module):\n",
     "    \"\"\"\n",
@@ -491,171 +1372,395 @@
     "        log_det_jacobian = torch.log(torch.abs(1 + torch.bmm(psi, u_hat)))\n",
     "        log_det_jacobian = log_det_jacobian.squeeze(2).squeeze(1)\n",
     "\n",
-    "        return z, log_det_jacobian \n",
+    "        return z, log_det_jacobian\n",
+    "\n",
+    "class PlanarVAE(VAE):\n",
+    "    \"\"\"\n",
+    "    Variational auto-encoder with planar flows in the encoder.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, latent_size, n_flows):\n",
+    "        super(PlanarVAE, self).__init__(latent_size)\n",
+    "\n",
+    "        # Initialize log-det-jacobian to zero\n",
+    "        self.log_det_j = 0.\n",
+    "\n",
+    "        # Flow parameters\n",
+    "        flow = Planar\n",
+    "        self.n_flows = n_flows\n",
+    "\n",
+    "        # Amortized flow parameters\n",
+    "        self.amor_u = nn.Linear(latent_size * 2, n_flows * latent_size)\n",
+    "        self.amor_w = nn.Linear(latent_size * 2, n_flows * latent_size)\n",
+    "        self.amor_b = nn.Linear(latent_size * 2, n_flows)\n",
+    "\n",
+    "        # Normalizing flow layers\n",
+    "        for k in range(n_flows):\n",
+    "            flow_k = flow()\n",
+    "            self.add_module('flow_' + str(k), flow_k)\n",
+    "\n",
+    "    def encode(self, x):\n",
+    "        \"\"\"\n",
+    "        Encoder that ouputs parameters for base distribution of z and flow parameters.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        batch_size = x.size(0)\n",
+    "\n",
+    "        h = self.q_z_nn(x)\n",
+    "        h = h.view(-1, self.q_z_nn_output_dim)\n",
+    "        mean_z = self.q_z_mean(h)\n",
+    "        var_z = self.q_z_var(h)\n",
+    "\n",
+    "        # return amortized u an w for all flows\n",
+    "        u = self.amor_u(h).view(batch_size, self.num_flows, self.z_size, 1)\n",
+    "        w = self.amor_w(h).view(batch_size, self.num_flows, 1, self.z_size)\n",
+    "        b = self.amor_b(h).view(batch_size, self.num_flows, 1, 1)\n",
+    "\n",
+    "        return mean_z, var_z, u, w, b\n",
     "\n",
-    "class FlowVAE(VAE):\n",
-    "    def __init__(self, latent_size, n_flows=20):\n",
-    "        super().__init__(latent_size)\n",
-    "                \n",
-    "        self.flow = nn.Sequential(*[\n",
-    "            Planar(latent_size) for _ in range(n_flows)\n",
-    "        ])   \n",
-    "            \n",
     "    def forward(self, x):\n",
+    "        \"\"\"\n",
+    "        Forward pass with planar flows for the transformation z_0 -> z_1 -> ... -> z_k.\n",
+    "        Log determinant is computed as log_det_j = N E_q_z0[\\sum_k log |det dz_k/dz_k-1| ].\n",
+    "        \"\"\"\n",
+    "        batch_size = x.shape[0]\n",
     "        z_theta = self.encoder(x)\n",
     "        mu0 = z_theta[:, :self.latent_size]\n",
     "        log_var0 = z_theta[:, self.latent_size:]\n",
-    "        z0 = self.reparameterize(mu0, log_var0)\n",
-    "        zk, accumulating_log_abs_det = self.flow(z0)\n",
-    "        self.decoder(zk)\n",
     "        \n",
-    "        return z0, zk, self.decoder(zk), accumulating_log_abs_det, mu0, log_var0"
+    "        u = self.amor_u(z_theta).view(batch_size, self.n_flows, self.latent_size, 1)\n",
+    "        w = self.amor_w(z_theta).view(batch_size, self.n_flows, 1, self.latent_size)\n",
+    "        b = self.amor_b(z_theta).view(batch_size, self.n_flows, 1, 1)\n",
+    "            \n",
+    "        self.log_det_j = 0.\n",
+    "        z = [self.reparameterize(mu0, log_var0)]\n",
+    "        \n",
+    "        # Normalizing flows\n",
+    "        for k in range(self.n_flows):\n",
+    "            flow_k = getattr(self, 'flow_' + str(k))\n",
+    "            z_k, log_det_jacobian = flow_k(z[k], u[:, k, :, :], w[:, k, :, :], b[:, k, :, :])\n",
+    "            z.append(z_k)\n",
+    "            self.log_det_j += log_det_jacobian\n",
+    "            \n",
+    "        z0 = z[0]\n",
+    "        zk = z[-1]\n",
+    "        \n",
+    "        return z0, zk, self.decoder(zk), self.log_det_j, mu0, log_var0\n",
+    "\n",
+    "m = PlanarVAE(20, 10)\n",
+    "m(next(iter(dl))[0].view(-1, 784));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def log_normal_standard(x, average=False, reduce=True, dim=None):\n",
-    "    log_norm = -0.5 * x * x\n",
-    "\n",
-    "    if reduce:\n",
-    "        if average:\n",
-    "            return torch.mean(log_norm, dim)\n",
-    "        else:\n",
-    "            return torch.sum(log_norm, dim)\n",
-    "    else:\n",
-    "        return log_norm\n",
-    "    \n",
-    "def log_normal_diag(x, mean, log_var, average=False, reduce=True, dim=None):\n",
-    "    log_norm = -0.5 * (log_var + (x - mean) * (x - mean) * log_var.exp().reciprocal())\n",
-    "    if reduce:\n",
-    "        if average:\n",
-    "            return torch.mean(log_norm, dim)\n",
-    "        else:\n",
-    "            return torch.sum(log_norm, dim)\n",
-    "    else:\n",
-    "        return log_norm\n",
-    "\n",
-    "    \n",
-    "def det_loss(reconstruction_x, x, mu, log_var, z_0, z_k, ldj, beta=1.):\n",
-    "    \"\"\"\n",
-    "    :param z_mu: mean of z_0\n",
-    "    :param z_var: variance of z_0\n",
-    "    :param z_0: first stochastic latent variable\n",
-    "    :param z_k: last stochastic latent variable\n",
-    "    :param ldj: log det jacobian\n",
-    "    \"\"\"\n",
-    "\n",
-    "    reconstruction_function = nn.BCELoss(reduction='sum')\n",
-    "\n",
-    "    batch_size = x.size(0)\n",
-    "\n",
-    "    # - N E_q0 [ ln p(x|z_k) ]\n",
-    "    likelihood = reconstruction_function(reconstruction_x, x)\n",
-    "\n",
-    "    # ln p(z_k)  (not averaged)\n",
-    "    log_p_zk = log_normal_standard(z_k, dim=1)\n",
-    "    # ln q(z_0)  (not averaged)\n",
-    "    log_q_z0 = log_normal_diag(z_0, mean=mu, log_var=log_var, dim=1)\n",
-    "    # N E_q0[ ln q(z_0) - ln p(z_k) ]\n",
-    "    summed_logs = torch.sum(log_q_z0 - log_p_zk)\n",
-    "\n",
-    "    # sum over batches\n",
-    "    summed_ldj = torch.sum(ldj)\n",
-    "\n",
-    "    # ldj = N E_q_z0[\\sum_k log |det dz_k/dz_k-1| ]\n",
-    "    kl = (summed_logs - summed_ldj)\n",
-    "    loss = likelihood + beta * kl\n",
-    "\n",
-    "    loss = loss / float(batch_size)\n",
-    "\n",
-    "    return loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 388,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([128]) torch.Size([250])\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "invalid argument 2: sizes do not match at /opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/THC/generic/THCTensorMathBlas.cu:20",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-264-2812b1a460b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cuda'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-264-2812b1a460b9>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch, m, device, optimizer)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0mz0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreconstruction_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccumulating_log_abs_det\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_var0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         loss = det_loss(x=x, \n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-262-fd36609523a9>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0mlog_var0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz_theta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatent_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0mz0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreparameterize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_var0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0mzk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccumulating_log_abs_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-262-fd36609523a9>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, z)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpsi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;31m# determinant of jacobian\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mdet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpsi\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# log |det Jac|\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: invalid argument 2: sizes do not match at /opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/THC/generic/THCTensorMathBlas.cu:20"
+      "====> Epoch: 1 Average loss: 1.1506\n",
+      "====> Epoch: 2 Average loss: 0.9609\n",
+      "====> Epoch: 3 Average loss: 0.9365\n",
+      "====> Epoch: 4 Average loss: 0.9236\n",
+      "====> Epoch: 5 Average loss: 0.9147\n",
+      "====> Epoch: 6 Average loss: 0.9082\n",
+      "====> Epoch: 7 Average loss: 0.9022\n",
+      "====> Epoch: 8 Average loss: 0.8978\n",
+      "====> Epoch: 9 Average loss: 0.8948\n",
+      "====> Epoch: 10 Average loss: 0.8918\n",
+      "====> Epoch: 11 Average loss: 0.8894\n",
+      "====> Epoch: 12 Average loss: 0.8873\n",
+      "====> Epoch: 13 Average loss: 0.8859\n",
+      "====> Epoch: 14 Average loss: 0.8842\n",
+      "====> Epoch: 15 Average loss: 0.8829\n",
+      "====> Epoch: 16 Average loss: 0.8817\n",
+      "====> Epoch: 17 Average loss: 0.8811\n",
+      "====> Epoch: 18 Average loss: 0.8808\n",
+      "====> Epoch: 19 Average loss: 0.8801\n",
+      "====> Epoch: 20 Average loss: 0.8803\n",
+      "====> Epoch: 21 Average loss: 0.8804\n",
+      "====> Epoch: 22 Average loss: 0.8807\n",
+      "====> Epoch: 23 Average loss: 0.9489\n",
+      "====> Epoch: 24 Average loss: 0.9029\n",
+      "====> Epoch: 25 Average loss: 0.8939\n",
+      "====> Epoch: 26 Average loss: 0.8916\n",
+      "====> Epoch: 27 Average loss: 0.8899\n",
+      "====> Epoch: 28 Average loss: 0.8895\n",
+      "====> Epoch: 29 Average loss: 0.8895\n",
+      "====> Epoch: 30 Average loss: 0.8896\n",
+      "====> Epoch: 31 Average loss: 0.8898\n",
+      "====> Epoch: 32 Average loss: 0.8903\n",
+      "====> Epoch: 33 Average loss: 0.8905\n",
+      "====> Epoch: 34 Average loss: 0.8911\n",
+      "====> Epoch: 35 Average loss: 0.8915\n",
+      "====> Epoch: 36 Average loss: 0.8918\n",
+      "====> Epoch: 37 Average loss: 0.8924\n",
+      "====> Epoch: 38 Average loss: 0.8929\n",
+      "====> Epoch: 39 Average loss: 0.8936\n",
+      "====> Epoch: 40 Average loss: 0.8946\n",
+      "====> Epoch: 41 Average loss: 0.8995\n",
+      "====> Epoch: 42 Average loss: 0.8955\n",
+      "====> Epoch: 43 Average loss: 0.8960\n",
+      "====> Epoch: 44 Average loss: 0.8979\n",
+      "====> Epoch: 45 Average loss: 0.8973\n",
+      "====> Epoch: 46 Average loss: 0.8981\n",
+      "====> Epoch: 47 Average loss: 0.8985\n",
+      "====> Epoch: 48 Average loss: 0.8995\n",
+      "====> Epoch: 49 Average loss: 0.9002\n",
+      "====> Epoch: 50 Average loss: 0.9008\n",
+      "====> Epoch: 51 Average loss: 0.9013\n",
+      "====> Epoch: 52 Average loss: 0.9025\n",
+      "====> Epoch: 53 Average loss: 0.9027\n",
+      "====> Epoch: 54 Average loss: 0.9032\n",
+      "====> Epoch: 55 Average loss: 0.9053\n",
+      "====> Epoch: 56 Average loss: 0.9049\n",
+      "====> Epoch: 57 Average loss: 0.9057\n",
+      "====> Epoch: 58 Average loss: 0.9066\n",
+      "====> Epoch: 59 Average loss: 0.9068\n",
+      "====> Epoch: 60 Average loss: 0.9075\n",
+      "====> Epoch: 61 Average loss: 0.9086\n",
+      "====> Epoch: 62 Average loss: 0.9089\n",
+      "====> Epoch: 63 Average loss: 0.9711\n",
+      "====> Epoch: 64 Average loss: 0.9523\n",
+      "====> Epoch: 65 Average loss: 0.9225\n",
+      "====> Epoch: 66 Average loss: 0.9195\n",
+      "====> Epoch: 67 Average loss: 0.9183\n",
+      "====> Epoch: 68 Average loss: 0.9183\n",
+      "====> Epoch: 69 Average loss: 0.9222\n",
+      "====> Epoch: 70 Average loss: 0.9185\n",
+      "====> Epoch: 71 Average loss: 0.9186\n",
+      "====> Epoch: 72 Average loss: 0.9193\n",
+      "====> Epoch: 73 Average loss: 0.9201\n",
+      "====> Epoch: 74 Average loss: 0.9318\n",
+      "====> Epoch: 75 Average loss: 0.9268\n",
+      "====> Epoch: 76 Average loss: 0.9218\n",
+      "====> Epoch: 77 Average loss: 0.9219\n",
+      "====> Epoch: 78 Average loss: 0.9221\n",
+      "====> Epoch: 79 Average loss: 0.9228\n",
+      "====> Epoch: 80 Average loss: 0.9238\n",
+      "====> Epoch: 81 Average loss: 0.9236\n",
+      "====> Epoch: 82 Average loss: 0.9244\n",
+      "====> Epoch: 83 Average loss: 0.9255\n",
+      "====> Epoch: 84 Average loss: 0.9280\n",
+      "====> Epoch: 85 Average loss: 0.9261\n",
+      "====> Epoch: 86 Average loss: 0.9266\n",
+      "====> Epoch: 87 Average loss: 0.9271\n",
+      "====> Epoch: 88 Average loss: 0.9281\n",
+      "====> Epoch: 89 Average loss: 0.9286\n",
+      "====> Epoch: 90 Average loss: 0.9287\n",
+      "====> Epoch: 91 Average loss: 0.9292\n",
+      "====> Epoch: 92 Average loss: 0.9299\n",
+      "====> Epoch: 93 Average loss: 0.9305\n",
+      "====> Epoch: 94 Average loss: 1.0064\n",
+      "====> Epoch: 95 Average loss: 0.9853\n",
+      "====> Epoch: 96 Average loss: 0.9660\n",
+      "====> Epoch: 97 Average loss: 1.2869\n",
+      "====> Epoch: 98 Average loss: 1.0245\n",
+      "====> Epoch: 99 Average loss: 0.9910\n",
+      "====> Epoch: 100 Average loss: 0.9951\n"
      ]
     }
    ],
    "source": [
-    "latent_size = 250\n",
-    "m = FlowVAE(latent_size)\n",
+    "m = PlanarVAE(latent_size, 16)\n",
     "m.cuda()\n",
+    "optim = torch.optim.Adam(m.parameters(), 5e-4)\n",
     "\n",
-    "def train(epoch, m, device, optimizer):\n",
-    "    m.train()\n",
-    "    train_loss = 0\n",
-    "    for batch_idx, (x, _) in enumerate(dl):\n",
-    "        x = x.to(device).view(-1, 784)\n",
-    "        \n",
-    "        optim.zero_grad()\n",
-    "        z0, zk, reconstruction_x, accumulating_log_abs_det, mu0, log_var0 = m(x)\n",
-    "    \n",
-    "        loss = det_loss(x=x, \n",
-    "                        reconstruction_x=reconstruction_x, \n",
-    "                        mu=mu0,\n",
-    "                        log_var=log_var0,\n",
-    "                        ldj=accumulating_log_abs_det,\n",
-    "                        z_0=z0,\n",
-    "                        z_k=zk\n",
-    "                        )\n",
-    "        \n",
-    "        recon_batch, mu, logvar = m(x)\n",
-    "        loss = loss_function(recon_batch, x, mu, logvar)\n",
-    "        loss.backward()\n",
-    "        \n",
-    "        train_loss += loss.item()\n",
-    "        optimizer.step()\n",
-    "        if batch_idx % 150 == 0:\n",
-    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
-    "                epoch, batch_idx * len(x), len(dl.dataset),\n",
-    "                100. * batch_idx / len(dl),\n",
-    "                loss.item() / len(x)))\n",
-    "\n",
-    "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(dl.dataset)))\n",
-    "\n",
-    "\n",
-    "optim = torch.optim.Adam(m.parameters(), 1e-3)\n",
-    "\n",
-    "for epoch in range(1, 11):\n",
-    "    train(epoch, m, 'cuda', optim)"
+    "for epoch in range(1, 101):\n",
+    "    beta = 1 / 90 * epoch\n",
+    "    train(epoch, m, 'cuda', optim, beta)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 391,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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TRMoKw87ZtxVfAXDazH485/LeOVf7JoCTtR+eiNTKQt6NfwjAdwCcIHm8uOxFAPtJ7gJgAAYAfLcuI2yQqPXmtVKiNks0BTaaRjo4OOjWvW2Vo+8djS2qDwwMuHXv9i9fvuweOzU15da7urrcutc2jKbXRktkL1++3K23ooW8G/9HAPP9NLdsT11Evkxn0IkkobCLJKGwiyShsIskobCLJKGwiySRZinpaEpimaWBu7vnnRbwFw8++KBbf/bZZ9367t273bq3/XA0hdVbbnkhxz/88MNufXh4uGKts7PTPTY692H79u1ufevWrVXVAKC9vd2tt+IU1oie2UWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSYJmtir/yjZHjAD6bc9FaAOcaNoCvplXH1qrjAjS2atVybPeb2bzrvzU07F+6cbLfzPqaNgBHq46tVccFaGzVatTY9Ge8SBIKu0gSzQ77oSbfvqdVx9aq4wI0tmo1ZGxNfc0uIo3T7Gd2EWkQhV0kiaaEneQ+kh+R/ITkC80YQyUkB0ieIHmcZH+Tx3KY5BjJk3MuW0PyLZIfFx/9yfSNHdtLJM8Wj91xkk80aWybSP6B5GmSp0h+r7i8qY+dM66GPG4Nf81OcjGA/wXw9wCGALwHYL+Z/U9DB1IByQEAfWbW9BMwSH4dwDSAX5rZ3xaX/SuA82b2cvGLstvM/rlFxvYSgOlmb+Nd7FbUO3ebcQBPA/hHNPGxc8b1D2jA49aMZ/Y9AD4xs0/N7DqAXwN4qgnjaHlm9jaA87dd/BSAI8XnRzD7w9JwFcbWEsxsxMw+KD6fAvDFNuNNfeyccTVEM8K+AcCZOV8PobX2ezcAvyf5PsmDzR7MPNab2Qgw+8MDYF2Tx3O7cBvvRrptm/GWeeyq2f68rGaEfb7Fu1qp//eQmX0NwOMAniv+XJWFWdA23o0yzzbjLaHa7c/LakbYhwBsmvP1RgCVVyVsMDMbLj6OAXgNrbcV9egXO+gWH8eaPJ6/aKVtvOfbZhwt8Ng1c/vzZoT9PQDbSW4huRTAtwEcbcI4voRkR/HGCUh2ANiL1tuK+iiAA8XnBwC83sSx/JVW2ca70jbjaPJj1/Ttz82s4f8APIHZd+T/D8C/NGMMFca1FcCfi3+nmj02AK9i9s+6G5j9i+gZAPcAOAbg4+LjmhYa238COAHgQ8wGq7dJY3sYsy8NPwRwvPj3RLMfO2dcDXncdLqsSBI6g04kCYVdJAmFXSQJhV0kCYVdJAmFXSQJhV0kif8HyXhFGrTdZ9IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_samples(m)"
+   ]
   },
   {
    "cell_type": "code",