vae_mnist_generation.ipynb

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   "source": [
    "# MNIST image generation with VAE (Variational AutoEncoder)\n",
    "\n",
    "Adapted from: https://github.com/Atcold/pytorch-Deep-Learning/blob/master/11-VAE.ipynb"
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   "source": [
    "## Cell color\n",
    "- Red: Related to model definition and training\n",
    "- Skyblue: Related to visualization"
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   "source": [
    "### Required Python Packages\n",
    "- `torch`\n",
    "- `torchvision`\n",
    "- `matplotlib`\n",
    "\n",
    "Run the following cell to install the packages."
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   "source": [
    "# package install\n",
    "!pip install \"torch>=1.9+cpu\"\n",
    "!pip install \"torchvision>=0.10.1+cpu\"\n",
    "!pip install \"matplotlib>=3.5\""
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms"
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   "source": [
    "### Load the Dataset (MNIST)"
   ]
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     "text": [
      "/home/jovyan/.local/conda/lib/python3.8/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
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   "source": [
    "# Define data loading step\n",
    "kwargs = {\"num_workers\": 4, \"pin_memory\": True}\n",
    "train_loader = DataLoader(\n",
    "    datasets.MNIST(\"mnist\", train=True, download=True, transform=transforms.ToTensor()),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    **kwargs,\n",
    ")\n",
    "test_loader = DataLoader(\n",
    "    datasets.MNIST(\"mnist\", train=False, transform=transforms.ToTensor()),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    **kwargs,\n",
    ")"
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show some images\n",
    "plt.figure(figsize=(16, 6))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    image, _ = train_loader.dataset.__getitem__(i)\n",
    "    plt.imshow(image.squeeze().numpy())\n",
    "    plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62a44cf-6cbe-41c9-8d0e-e4ed17c91a7f",
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   },
   "source": [
    "### Vanilla VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abe73dae-f8a5-4d36-9fbc-c6cb5e51295e",
   "metadata": {
    "canvas": {
     "comments": [],
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     "isComponent": true,
     "meta_id": "abe73dae-f8a5-4d36-9fbc-c6cb5e51295e",
     "name": "Define VAE",
     "parents": [
      {
       "id": "c1c9d3af-d77f-40cd-b145-3976174d3032",
       "name": "Load MNIST dataset"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defining the model\n",
    "\n",
    "d = 20\n",
    "\n",
    "\n",
    "class VAE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.encoder = nn.Sequential(nn.Linear(784, d**2), nn.ReLU(), nn.Linear(d**2, d * 2))\n",
    "\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(d, d**2),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(d**2, 784),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "\n",
    "    def reparameterise(self, mu, logvar):\n",
    "        if self.training:\n",
    "            std = logvar.mul(0.5).exp_()\n",
    "            eps = std.new_empty(std.size()).normal_().detach()\n",
    "            return eps.mul_(std).add_(mu)\n",
    "        else:\n",
    "            return mu\n",
    "\n",
    "    def forward(self, x):\n",
    "        mu_logvar = self.encoder(x.view(-1, 784)).view(-1, 2, d)\n",
    "        mu = mu_logvar[:, 0, :]\n",
    "        logvar = mu_logvar[:, 1, :]\n",
    "        z = self.reparameterise(mu, logvar)\n",
    "        return self.decoder(z), mu, logvar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0cfdabf-bd6d-4332-94d7-83fe12695584",
   "metadata": {
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   },
   "source": [
    "### Define loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2ba2545-1905-40be-bc8d-18ef0ca782e6",
   "metadata": {
    "canvas": {
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     "isComponent": true,
     "meta_id": "e2ba2545-1905-40be-bc8d-18ef0ca782e6",
     "name": "Define loss function",
     "parents": [
      {
       "id": "e9f3371a-e347-4564-80c1-f4e1a17da0d3",
       "name": "Define VAE"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Reconstruction + β * KL divergence losses summed over all elements and batch\n",
    "\n",
    "\n",
    "def loss_function(x_hat, x, mu, logvar, beta=1):\n",
    "    BCE = nn.functional.binary_cross_entropy(x_hat, x.view(-1, 784), reduction=\"sum\")\n",
    "    KLD = 0.5 * torch.sum(logvar.exp() - logvar - 1 + mu.pow(2))\n",
    "\n",
    "    return BCE + beta * KLD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b2c753-d3ce-4f09-adcd-257c5a515c0b",
   "metadata": {
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     "id": "3810673c-b19b-4c9c-a3b5-3462a02080a7",
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     "name": "",
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   },
   "source": [
    "### Visualization function"
   ]
  },
  {
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   "id": "85c5dbbb-532a-47f0-846d-7b7aef911881",
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     "meta_id": "695c7b84-0c69-4f3f-91f6-fbc995b565fb",
     "name": "",
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    },
    "tags": []
   },
   "source": [
    "### Training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81c54f25-8b86-4aeb-9d9b-0982452bdd7b",
   "metadata": {
    "canvas": {
     "comments": [],
     "componentType": "CodeCell",
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     "id": "64bafb82-5b64-4894-a09d-9a4e35202f04",
     "isComponent": true,
     "meta_id": "81c54f25-8b86-4aeb-9d9b-0982452bdd7b",
     "name": "Define image display function",
     "parents": [
      {
       "id": "341de75f-bacb-4af8-9269-f974f4547ca8",
       "name": "Import packages"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def display_images(in_, out, n=1, label=None, count=False):\n",
    "    for N in range(n):\n",
    "        if in_ is not None:\n",
    "            in_pic = in_.data.cpu().view(-1, 28, 28)\n",
    "            plt.figure(figsize=(9, 2))\n",
    "            plt.suptitle(label + \" – real test data / reconstructions\", color=\"b\", fontsize=16)\n",
    "            for i in range(4):\n",
    "                plt.subplot(1, 4, i + 1)\n",
    "                plt.imshow(in_pic[i + 4 * N])\n",
    "                plt.axis(\"off\")\n",
    "        out_pic = out.data.cpu().view(-1, 28, 28)\n",
    "        plt.figure(figsize=(9, 3))\n",
    "        for i in range(4):\n",
    "            plt.subplot(1, 4, i + 1)\n",
    "            plt.imshow(out_pic[i + 4 * N])\n",
    "            plt.axis(\"off\")\n",
    "            if count:\n",
    "                plt.title(str(4 * N + i), color=\"w\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b1bc35f-3ea6-43a3-a0ec-5247ed17afce",
   "metadata": {
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     "isComponent": true,
     "meta_id": "3b1bc35f-3ea6-43a3-a0ec-5247ed17afce",
     "name": "Train VAE",
     "parents": [
      {
       "id": "e93082b9-135f-4c0c-9ddf-a8b788599667",
       "name": "Define loss function"
      },
      {
       "id": "64bafb82-5b64-4894-a09d-9a4e35202f04",
       "name": "Define image display function"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Test set loss: 543.7519\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 1 Average loss: 190.0560\n",
      "====> Test set loss: 137.4955\n"
     ]
    },
    {
     "data": {
      "image/png": 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KarQ/qZl5P9/RxT9sVTUE+fIlQ9y6to9puvNCVty5k4sX/tS/t7re9q6LyzdsqLtj9+/j4r2enOvi6zt94OKyUO7ib8rXu/jUbgfVWd5qQzUEIiIiogKBiIiIqEAgIiIiNOI+BOHzxDwEBzdMPqT+LbnITyl73XnjXfxvrVYntmjuotR+A+WUk83YTu+4eM/B/kbrMSHr5ptbvMyFM387yMWt+GcNdyjZlBzl+wXMPvVOF68or2pX3mzeAdSHoJCkPqoaYNV4/z/g3YH+cdXH/2m430Ed9iHo/LB/H4/d4X0Xbwo+/T2rerv47j8f7eJuTK2zvNWGaghEREREBQIRERFRgUBERERoxH0Ieu3rx28XYS5e+rYfb9wTzUOwtbjl/HtdfHCLumsbzLeyb791caun1WegLhUN2s3FZ9/6tIvL8Y27I268tPLvHR6blr+MSa3Z4/7765R+TzVQTuD4Du/VKP3zp/u+R93eKYw+A0mqIRAREREVCEREREQFAhEREaER9SEoau3HnE7c9TkX93v6XBf3mdx42pUlu++/6/uHDG2x0cXZZxKAq5f5segfnFw1D/mmu/2+Ju7q25ylcZl9rv+cOGk7P1586KyTXLzjk3Mq/87+VAupb3Mf2MfH/e7NkDJy1bK9XRxKNmZIKZmohkBERERUIBAREREVCERERIQC7kNQ3KG9iz/75S6JFK+76Plj/TPvL73lxy4urbOcSb6VDxvs4jEd/BzlTW07Fy8v8/1F/vOL41y85ng/9rxsxWeVf2/6b9+/oOj+7GXk3o8s9gsS92nZipVZt5e6FQ7Y08VPDL/LxR9t9D1M2lzn753Ue0EaVsnR/r1477DxWdM/v66ti1P7BgGUf6fXtqZUQyAiIiIqEIiIiIgKBCIiIkIB9yEoX+3nfO9z82wXzx5V4uJRM890caf5Pr00Hosu8OOHuzZp7uJNwY8Yv3rJ4S5effCKnI/VetZXLn5gdXcXn9F2gYvv+/sjPv03+7r4fycc4uIeYwtzzvKtxTdXrnfx3s2KXfzbFQNdbFNn5j1PsmX2vvYdFx/aMvtcMhe/MMrF/eZOr/M8bWtUQyAiIiIqEIiIiIgKBCIiIkIB9yEo6t/bxZ+e3sHFf1jq25nXz2yX9zxlYnvvnnV9eOejesrJ1uHQ7p/W27GWjujh4sNaJ5+x3sxFnYtbuvjyjr5NeuLee9RZ3mRzRW3auPjO3R9LpPB9CCbcfqiLOzAt52NZE//xWL6ff23nnd7UxW3m+LjrQ74fk+ao2NySiw6s/PuvnW9JrG1KNt1eru4pJltuwzG+b9DyM9a6eHjLtxJbFJPNXvd/4OJJ9xzkE/ipUuj8rJ9DoWypfyZHvqiGQERERFQgEBERERUIREREhALuQ/DJBd9z8a+HPeviCUsGubh4veUtL9+dsr+Lj7/yFRdf2t6PTS82X84a8OZpLu5+om9PEu+Fj32fjJu7vJE1/bgd/esxfd4OWdMXW1Xb48Bmft+di5snk9fI6lm+r0v7xPrivr1cPHdMZxf3uVRjqbNZfIafV2BwsykufrPEv/c6/E/ufQaa9Ojm4nljurr4o9PvyL6DY3x451l+bv2/7f69nPOyrShP6aLT3LL3Gdhs26b+M794t/412n7+SR1dPOCweZV/X9P1brdu780+FrL3GUi6ptN7Pr76vQwpIwOPGO3iHiepD4GIiIjUExUIREREpHCbDNrM9VlbsJ+v3vlick8Xd/1t3U0R23TKTi5+pNd/u7h7Ez/0bLPBL8EveXDv8S4ey961yd5Wr/9tflrqlw/yQ81GtFzn4nZFLVz8w1bfZd1/EVVVjeW0zJIy+7YAz6z192W/+/3jkTd77Haxr2ocMcxXHc5DslnTPftQs9Gv+ynM+/Fuzvv+5Frf1DTviDtdvKDUT5M88o7/cvHAkZ+4+KGeL7v47ht+4eKeV+benLG16vxW1fTEL61v7dYNb7k2mdxpdZ6fdvzZXZ/NkLLwrAt+2Pw+f77YxS2X5K8JPBvVEIiIiIgKBCIiIqICgYiIiFDAfQh2mrrGxR8e38XFB4/0ba8LbtzyY829x09T+XIvP4Vmss/A2GWDXdy0yD+O97jtfd5Om/5zF/fm/S3J5jYjOdXz7Sf/yMXnn+/7DPxsLz908JIOH1ZzhKpycPnmPUBy3hZgyuoBLi79fEGN9lZOw7QVNlZnDn816/qmC7d82Oj4H/zJxSvKfZ+BH4+9zMVdxvt+S6sf8UNI+187xsWTRvm+SBc+cZaLy2dte49sL55S1cfjordOces+HvZA1m0n5rHPwGVL9nPxzJU7u/iRXfyU2ckpzZOGn+XvhVYf+b5GvRcWRn8S1RCIiIiICgQiIiKiAoGIiIhQwH0INrb1j50t3eTjGg4fp6hVq8q/vzxvkFv36bF+StL1wV+WXSb7PgCs9evHHf5XF9/39TAX9z3bjy7P30M7t07JPgX9Rvv1b3Tw7XuThwzNed/LB/rpUmdc9Mca5U0at9T+Q/s084+0PexXl7i43WPZ23lLlyx1cccubV3cJ9EXafEwP7F151nZ87q163fBQhfv+8CpLp6+j58ifkPws3y8V+LnMajOWdN+6uKOk6r6JnV83c9xsHqE78PGFTU6FM2/9nOnlC78smY7qCeqIRAREREVCEREREQFAhEREaGA+xAs3cf3Gdh+o49fW9DXxT3I/kjhtUfuUfl30WYTzHuDH/2li/td7tsOf/jRKhf/uI1vO3zz234uLl+bfU5uqZ2yFStd3OzFlRlSbq6DDanr7EgePfHA4S7+1X/55weUtg412t+Yg6rmNRj46jlu3S4vznWx7ZxoRy7z84/M/5l/3PEHg33fpD2m+jbr7rfX3fNXtgZly1e4uNPxPt7tlvNd3Gy1/z7b/ZqaXc++ZH4EcfJfxLd9fD+l6uYduHDRQS4uXvJN1v0XCtUQiIiIiAoEIiIiogKBiIiIUMB9CLrdkL09KJx2QNb1yecT7HZd1bjPxQf0cOuOmn2ci8u28zMFHDjTP7v66S/9swzueu6HLu7168KYl1pqrqiaMnJR4tkDRdSszTpp0bq2iSUb0qaTSJdXff+Qub/012vaf9zs4qNnXeri9g/692ZZqHq9Zx92v1t3zz96u3hY6zku/rrMj3sf2uIFF08v8fdK9xOz93OS7PpePL2hs5DRuuD/R7z+5F4u7vJV4+gvohoCERERUYFAREREVCAQERERCrgPQfFu/V28+NCOLu44088NndR6vj+18pSx6qFJd7du2A7+WQOlwZeT5qzxzznfWFbs4uYr9Ez7rUV5tU+a8PdGOTV87Rcvc+HGs3dIJFiKZFY+a7aLRz7qnzcw58y7Xfz4uJtcfNkZJ7j45LbPpER+bPn57b5w8abgn3sxoKlvN076zRj/DJSmzMiaXhqvL0r950CXmxpHn4Ek1RCIiIiICgQiIiKiAoGIiIhQwH0Iyj7284i32r2diw++0z+7fPy0g13co/ciF88dOKDy79mH3unWPZ8YC35G/3dcfMwNl7m4xUrfztzlqcbZXiT1r+zbb/2CZCw10vMqP69A/6JzXfzRT/3zBJ7o82JiD5nnpC8L2fuTvLq+hYuv/dWZLt7uVT9Xfu1mrJBCdsYHp7u4I3MzpCxsqiEQERERFQhEREREBQIRERGhgPsQJG331D9dfNUfPnTxFcdmnyd89YCqOc/vWbWbW1dkvq3wBxNOc3G/h3xbYPkGzTcvUoh6X/22i0fe5+cdmH9qVxd3P7xqroG5C/18I2G9/3jsOsmPNW8zdb6LWy/1n1HqM9B4Nem6s4v//B+3JVL479L2XIc856h+qIZAREREVCAQERERFQhERESERtSHIGnA66Nd/Mz+97q4f9NmLj5x9k8q/252pJ+jPKkPfmxzdbPby9ajqJoyclHi2QVFaikuKKG01MWlC/7l4m43+DjcUPV3P76q0bHKapY1aUya+n+Ng5ttG9+dt42zFBERkaxUIBAREREVCERERKQR9yHodcosF1/MAVnTNyN7vwHZNjVb7Z9pP7/UzzHRo4nvi5IsQ3dpvsrFn3fw45fLVqysVf5EROqLaghEREREBQIRERFpxE0GInXBps508anXXOriv467ycU7FfvH5V7SwU+h/X+P7uHidif7YXCbPf5YRApO2aIlLt5l8s9dPOfI+1z868sec/GV3U51cY+r/VD2QqUaAhEREVGBQERERFQgEBEREdSHQMRp/yff1nfYbr5PwUmHT3Xx2E7vuHjZJzu4uO06DXcVaWxCSYmLe4/3U5TPGuYnrt6vxSIXd3ljU34ylmeqIRAREREVCEREREQFAhEREUF9CESy6nPpdBe/kyhDH8cQF/fFp9fDkUUav+Ip77r4il77Zk3flBl5zE3+qIZAREREVCAQERERFQhEREQEFQhEREQEFQhEREQEFQhEREQEFQhEREQEsBA0UlpERGRbpxoCERERUYFAREREVCAQERERVCAQERERVCAQERERVCAQERER4P8BcCEJpJLREq4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 2 Average loss: 133.8396\n",
      "====> Test set loss: 116.5032\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 3 Average loss: 121.7237\n",
      "====> Test set loss: 109.5354\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 4 Average loss: 116.5905\n",
      "====> Test set loss: 104.9082\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 5 Average loss: 113.7427\n",
      "====> Test set loss: 102.8385\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 6 Average loss: 111.8959\n",
      "====> Test set loss: 101.6415\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 7 Average loss: 110.5948\n",
      "====> Test set loss: 101.0912\n"
     ]
    },
    {
     "data": {
      "image/png": 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6/c3F/f45wsU2o62L92i1PS/5aspUQyAiIiIqEIiIiIgKBCIiIkKe+xDY4QdVvL7okUlu3fDitVm3PeT+y/2CYOkT5qjjrMr2oqIn/1WrfcnO6+bhD7i4bbPsfQaqsvJoP3St7UO12p1U07d+8IKLm5H5c2TrLzsl0n6SlzxJ3VhxQvb1RU+3cXG7sa/lMTc7B9UQiIiIiAoEIiIiogKBiIiIkO8+BAs/qnj99pd+PPAZxauzbjv7Yj9FbBm1m0Z0+fatFa/n3tgxS0qYt6WLi18c4R+rGd6cW6u8SONR0L6di1tZSZ3u/8Bb/H1eWqd7l6SVVxzp4kG7JT9H/EQRY7+onP62rNB/P9K3pcalsLPv4/HMabcmUvipituNnZHnHNVc8lHNu72X+JxooPlKdM+LiIiICgQiIiKiAoGIiIiQ5z4EZRs2VLx+4Wbftjf0ulku7tcyv48a7VZYVPF6n8L1WdOe3Nqv7/6wb98Ze4DvDyFN1+Ir+rh4UNHUBsqJ1IWyb37u4v4tQ4aUkUeWH1HxusW0N/OSJ6kbpV06uLhX85YNlJOqbTv5ay7uce0CF9/X9W4XP7jB94/484CDXFz6ub+v80U1BCIiIqICgYiIiKhAICIiIuS5D0Gq5LPFf7L6Uhdv6Fq9rGzo5uPfnJ19kvihxTVvgxm95DgXF6Nn2jdlm4dVjgGe/90xbl1JqF0Z+dpVff2CdRvSppP8GNilen+bH83uXPG6Fx/WdXakDi06ZzcXJ+emOX722S6uz8/p7ccf7uLr77rHxV9r6WcgGf7+6S7+S6+nXfzA189wcYspb9Q2izlRDYGIiIioQCAiIiIqEIiIiAj12IcgKdkm0r6a2yfT3z/6Gy6e9ys/V8DQ0/y4T9l1LTu1cmx6SfBte7V9ZsbTY49xcadVegZ7PoWj+rr4mr38swua0drF0zb7seu9x62reJ185wsOOsDFHw71nzptF/stvjLH91MqnevHnkt+fbzyKy7eP4/HalZc7OKWP/vExQc03+zi85cMdfGWq/fyO3zch8tOLHDxflNqkMkaUA2BiIiIqEAgIiIiKhCIiIgIDdiHoK59fFYvFy887U4XN7fKNpmS7NObs2T7Fhc3u7tDIoXmIWhKCnv4SSvu+ub4Ott3n8kjXdz7dvUZqE8rBvo+Ah0Kilxchv9jv2T6hS7u/e7Mitdbhvhn1P/o1oddfFriGSfNMBcv3b7JxSc//mMX73eVn4tF6lbBJ/X3bIMP7t/PxbN73+/i/1w6xMXrj17jdzBgn6z7L/q0Yb6rq4ZAREREVCAQERERFQhERESEJtSHoOAA30egy3g/7vPezr93cRm+PSm130BVY82HvObbhXfv6MeEthzUz8XNps/Kuj9pWAtHdnHxcUUbU6LalYlbrWheq+2ldi4+t3oDtFt91MLFW085ouL1xLtvd+tam0+bNLdkm4sPbO77L9w0dIKL7/nTaS7WPAV1q+eTX+Zt3836/puLpw7wz0BZ5acz4aNberu4mH9W63jdHv3IxdurtXXNqYZAREREVCAQERERFQhERESEJtSHINlnYPQ+LyVS1N0Y1NkD7/MLBvrwlS2tXHzLsae4ePuy5XWWF6m+5Bz0Vw2ZWGf7nrfN9z/pMn1LhpSSDxvP/LqLDy26L0PKyMpSP6d8wVa//ujfVs4NkOwzcNvnvh14wr0nu7j4U38vTL/lDy7uUejHntu6DVnzKlXw0z7QrB6/z75/TlsX71Po+4v0eewyF/f6a/Y5J97/nu+XVp/nkk3jyIWIiIg0KBUIREREpOk0GUyb18cv2KHJILtnN+1e8bok+NNuX7DRxQNaJeoVEwa28sONrr7LT5+65//6qsbS9xbmnE+pvU3d2rj44rYfJVJUloNTp7SGqqe1Pv9tP/Xt3i++Ve38Sc21nfWZi49tVZJI4euVW5mPWx612sXX7vlOxm2nfeY/czolpqUu7LK3i+eV+Lwc0sI3Qcz/sZ9Cu9ePPkaqIfG3+dxm/wjiwpV+aum6HKr33rn+sdrJoes9n8j+PyNpzJEPufjCpSe4uPSzVdXaX11RDYGIiIioQCAiIiIqEIiIiAhNqA9B8Vw/rPCJAR1dPHw337Z4/Oyz/faDMz+yuLBnDxcvvsC3DfY7cZ6LH+jxdxeP6DnDxaP/43QXd7tOfQjyKTmt9eHXv+nibFNVJ/sMJNP+bKV/JO4+FyxLpJf6VNph96zrk487bm1+aumpfccmtqgcPvb8Zj+UrOBC36cg2SYdvuLzUmw+RRn+2K/8+00uHvGjo5GaO6HID+O8Yy8/NJAl+Tv22lLfZ6DF4pUuTt4r70/w090fX+Q/o6670w+V3n1rwzwqWzUEIiIiogKBiIiIqEAgIiIiNKE+BHvf6McAj72xu4/xcTGZ+wwkbV+81MXdRvl4+du+Hbn5mBdd/N9tffqbOyaehSl5lZzW+jedqveo0Wxe+OM3XNxhw4wMKaU+bOxaVHWiFMl5Jtom4lTfm3aBi3sveyPrvpPzXXQoyLxvgPMWnuPiQpLzY0htfHjKbi7unsdm+KHvXuTidit8P7ENZ/vPjUlH3+LiPn++0sev+unu6+txx0mqIRAREREVCEREREQFAhEREaEJ9SFoSMtO9WObS4LvI5Acu979afUhyKeC9u1cfGb7l+ts30PnD3dxp0kfurih2vYk0mbB+qoT1VCX7muqTpTiw6F+noLk45OTls7285v0Uh+COrWtR8M9inzzGb6f2f03+D4DPZv7OSl6/ch3cGgsnyuqIRAREREVCEREREQFAhEREUF9CPJi/b6+vWjPBsrHzqrDJN/iNqhoU53tu+BS3w68fUXu81lI/Ru54igXj+nyarW2L7DM34matW7t4vk3H+ziJWfc4+LS4PsULCzxbdp97vzUxY2l3bip6Pyq78vV7Gz/3i044V4XD+t8mou3f+KvfzafjTzSxc3tbRe/1u8Rv8EYH35S6vuVHXPNFS7eg8Y5n4lqCEREREQFAhEREVGBQERERFAfgrQKu/jxwoW7lWRN//F2/2zs1sP8s7G5u06ytctaeO8RLp7a3bcVloSal2t7T77Ux4uyz18vDavs3fkuLirwH2EbQ+JvsYq5AQiVbb3PHPyQWzVnTksX92/p57tI9hmYvc1/TvzveSNdbIvfzp4XyarNP3x/nmc2tXXxKa0/d3HLx/x8MNsH5n6sjmP8s3NKrsk+90zS8a9c7uKe4xpnn4Ek1RCIiIiICgQiIiKiAoGIiIigPgRprbqn2MVz+t6bSOHLUZO/PNDFba70l1VPNqieL87xzxKfetLNLi4JrVxcVXte0q9WHVbx+sCfLnXr9F41LfMO96P5B/z8KhfPuMTfO9n6FCTX9W8ZMqSM3L2+u4tvmzjExb3enePi6t2lklS6apWLb/rJuS4+7rbbXDy+59Mu7vew7y/U/hn/ObL78sr+Jxuv/iJx9Dez5q3PxMt8fFXTfO9VQyAiIiIqEIiIiIgKBCIiIoL6EACw5HcDXDy37+hECl9uam4FLn7mDD9OvnTRwjrL266goH07F3cducjF3QurGEtehbWlfmz65LuPrni956qmMT5YctP1V378+Bkz/RzyH57u5w44/JDKse2P9Jzq1q0s3eziQX/9sYuTzyboudjfS02l3bipKn78ny7uf+iVLr79O39y8ZxBib5gg6pztOzfnUceM83Fj51zkovb39c0PmdUQyAiIiIqEIiIiIgKBCIiIsJO3IegbFA/FzebPqvi9brzfZ+BK4dO9NtW0fp3xFvfcXHHjckxq1Idm/rv5+In9h2TIWXNjF13uIv3vLtptOdJ7bV81j+bovezfv2GlNdD8PdJUi9ed/H2DOmkYfT4uf+7vnPsKS7+/n93dvGAQXNd/MrCXhn3XTTfz1nw+4t9/4TvtHnHxX/qcrKL22fcc+OiGgIRERFRgUBERERUIBARERF24j4EzVd96eJeM5tXvP7ZXje5dW2bVW+ce7shfp4BtSXWr+Pn/IeLV8zdy8UtvvDl3O5PrU/sYS4isnPbvnipi3te7eOVifT781bO+77jt32yru/Ga1nXN1aqIRAREREVCERERGQnbjJY3d8P9Hiq8yMVr8vI3kQw4YuuLr7rluEubo+GrdWl5NCwoV2OyJAyUsQSF/dKxEnZH2IrIiKgGgIRERFBBQIRERFBBQIRERFhJ+5DUB19Jo908QFjNrm4/Sz1GRARkZ2baghEREREBQIRERFRgUBERETYifsQ7DHOt/sPGZf50aa98ePgNW5dRER2NaohEBERERUIRERERAUCERERQQUCERERQQUCERERQQUCERERQQUCERERASwEjboXERHZ1amGQERERFQgEBERERUIREREBBUIREREBBUIREREBBUIREREBPh/KGHtxkAZGfcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Epoch: 8 Average loss: 109.5482\n",
      "====> Test set loss: 99.2361\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training and testing the VAE\n",
    "codes = dict(mu=list(), logvar=list(), y=list())\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = VAE().to(device)\n",
    "optimizer = torch.optim.Adam(\n",
    "    model.parameters(),\n",
    "    lr=learning_rate,\n",
    ")\n",
    "\n",
    "\n",
    "def train_and_test(model):\n",
    "    for epoch in range(0, epochs + 1):\n",
    "        # Training\n",
    "        if epoch > 0:  # test untrained net first\n",
    "            model.train()\n",
    "            train_loss = 0\n",
    "            for x, _ in train_loader:\n",
    "                x = x.to(device)\n",
    "                # ===================forward=====================\n",
    "                x_hat, mu, logvar = model(x)\n",
    "                loss = loss_function(x_hat, x, mu, logvar, beta)\n",
    "                train_loss += loss.item()\n",
    "                # ===================backward====================\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            # ===================log========================\n",
    "            print(f\"====> Epoch: {epoch} Average loss: {train_loss / len(train_loader.dataset):.4f}\")\n",
    "\n",
    "        # Testing\n",
    "        means, logvars, labels = list(), list(), list()\n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            test_loss = 0\n",
    "            for x, y in test_loader:\n",
    "                x = x.to(device)\n",
    "                # ===================forward=====================\n",
    "                x_hat, mu, logvar = model(x)\n",
    "                test_loss += loss_function(x_hat, x, mu, logvar, beta).item()\n",
    "                # =====================log=======================\n",
    "                means.append(mu.detach())\n",
    "                logvars.append(logvar.detach())\n",
    "                labels.append(y.detach())\n",
    "        # ===================log========================\n",
    "        codes[\"mu\"].append(torch.cat(means))\n",
    "        codes[\"logvar\"].append(torch.cat(logvars))\n",
    "        codes[\"y\"].append(torch.cat(labels))\n",
    "        test_loss /= len(test_loader.dataset)\n",
    "        print(f\"====> Test set loss: {test_loss:.4f}\")\n",
    "        display_images(x, x_hat, 1, f\"Epoch {epoch}\")\n",
    "\n",
    "\n",
    "train_and_test(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690c045b-f07c-4e63-a608-58abcab5cb88",
   "metadata": {
    "canvas": {
     "comments": [],
     "componentType": "CodeCell",
     "copiedOriginId": null,
     "hasOpendPDC": false,
     "headerColor": 0,
     "id": "55c262f8-927b-4fa2-a144-902d93cec844",
     "isComponent": false,
     "meta_id": "e6dfc6fd-e0b1-473c-9cec-8158774b0185",
     "name": "",
     "parents": [],
     "preDefined": null
    }
   },
   "source": [
    "### Generating few samples with randomly sampled vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84fb2ab9-1619-4b11-97f8-1bd8dab6d5d2",
   "metadata": {
    "canvas": {
     "comments": [],
     "componentType": "CodeCell",
     "copiedOriginId": null,
     "hasOpendPDC": false,
     "headerColor": 5,
     "id": "38bb92df-0247-47ce-bc3b-57ffbd90a436",
     "isComponent": true,
     "meta_id": "84fb2ab9-1619-4b11-97f8-1bd8dab6d5d2",
     "name": "Generate few samples",
     "parents": [
      {
       "id": "73fb295b-17e9-4ca6-a42f-02720231688c",
       "name": "Train VAE"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1X+KM63/ie1YESVd/UlsrsTXpsdV2lcbBrSckfv2C3FoJe3sa5bbeWu27o9UINmXnT64QAAAABgQAAIABAQAAsFKuIXDrnrdernndj12v65K/pUZrCKpCN2c11pqD7cN6+3/94maJF2/RmgGcoQLm0QN37PSua5L4zUt+J3HCNFc4FOuc38FY5ytvH9Zc4sgmzf9F/Xos4izK6Ps46O49zR1PccdCVKf9So6+SvO8N9/2sMRLyjTv+46fvVfi1Zu1TwHOkF+vYE7N6NcjS/V91qdpdov79eOs5lmNGx7VurKoT9dTCdxzB1Vao2CLte/BSFONxMfcZ9S5b9TzwKeW/q/Ew3l/j3/TXiG3bU/rWjuFwhUCAADAgAAAADAgAAAAVsI1BEG59ovvfbn2qb6hSud6z01oPsdLurnkK8p0LvnQPJfjDhgrnRE/Z7qQNQRubvlIje7LXx5ZJ/GxoTqJ+zJ67K2r0bnoI5G+bVyLC46ds8kdR/GI6z/f7eb9+7ULqjSvO7BY17FYdfseid/X+JDEHW5fr/mW1izEhVw/YMx7aGr62U+pCXop+PUKbHFubYljV7r1atZ2631P6L6uPupenzL3vq3X931coc8d1bkaAtcDY6KX/4bGpyVeltRjJWO5Yzl0nW0qTro1OQq0Vg5nLgAAwIAAAAAwIAAAAFbCNQThHO0Tvaipc9z7+z4DXm+kfa6HXT7u7970C4l//MCrJU7+Zos+YCFzh0XMx79ofht9Hn2C/XNG3NoFoet53j9ft6X8Hp1P/MxOzU2Gg5q/27n+Aok739Cnj+eWPgjn1UucaWvPBaW470rZRPUY/vWMNLEbVGjPiPb1mif++KLfSHxOUmuReoZ1rnp4oE3iAp4Fpsd5YCK+xsOtNRGmtGv/wDm5z4HoWl234opFByR+ulr7jXS4/iMjNdpzIjHk1rVwh1rlST2WandqT4rwpO79sl7ddr9+Tuj2X1/esfpcr66dU37wpMTpqDD7misEAACAAQEAAGBAAAAArIRrCPy8S59Bac1o4rbd5Q5/3LlB4v954nKJP3ntjyX+k9odEodf0Me794Ovk7jywa26fW4t7TPK703DXKGfP+xzgZmJ1onP/x0nOVc50aBr2B997UKJB5r19Vv4u0596qe153js8nWNvTp3vWOdPn7fYj1Wuq89V+La+3M9L6KJXgeMK3Bzyf1aBb5moPdibXjfdMNhicsCzQP7dSzaI52L7ntcYJL8e9vvP1d/07Mk93pXVWgNwdJUh8R9ae0fMvdq7TWTvFbfp52D+hly6BmtOWh4Qv9erqnQYy8Y0Mfrc8sPnFeh/Uu8I5ncsbr3kWVy26rj+nlkcWF6TnCFAAAAMCAAAAAMCAAAgJVwDYGfn9rZp/mexwZWSPztFq0RSH1Z88rn79Y5pJ9pv13iK9/2eYlvqdkl8U/vukjiTLf2wy976lmJo/7c/OU4U9DZykUR1mnfiDHzi12NRZxx88UTubFpUO3ztpobTC/RPgP7btLnLtugc3gTj+mxEJ7QHugZv39cXtPXcCSGNA+68OJjEh8dbJZ4zlN567i7NdgL2s9iusjLM4eVWhMwJoc/X+dvjzRrf/rDr9K/ef68SddAeWbI7atgv8Q/71qvz1ehx2JBTYNaojMVu/2bHMj9zq1H6uW2p2q0PqQyqfUfVzdobdClVfsk7ov02PpW5dUSb6vWooA4oeeR3qUS2ntu/ZXEq5O67kW/KwP43KE3jX694gdaW5T/+ZF9cvoQAACAAmFAAAAAGBAAAIBSqiFw81Oj+fUS16Z0LYLfd66UuP8hzSvPf1zzR9HJTonP/ZHmqTde90aJP7TolxJf16i5x6+9VfNXyytWS1y57eDo13GX5qyjYc11zYg8ss+7+9xvSmtAglD3d1Cb6yHfv9b1JD9P87a9l+r84stW6Lrjz3Q0Spxqcck7v7a4nwvt6h/SCzQvPbRAf/78uVpDcGiB9kjPzMv9bkGL/t4Fml48rQR5+yNs0n3pj43BebqvTlykr++y9dp34CctF0rcs0fzwnHzoMSpHXrcLg2P6sa6dTRmxHu5kHz9TkenhHO35WoK4lD31dYyPQdfuqpF4lUVrRIvSepj90R6Xnn7woclPtSg9SmDG7S+YSjWY++SlD5/Z6R/bz/Yd77E+7+R+8xo3L5Zbov8OapAuEIAAAAYEAAAAAYEAADASqiGIHQ55vZLNW+7oXGLxKmE5uF3aYmBxenx55YnOnSO6BOHlkjc0qT5o3UpzUW+8YonJf5ZWtdOWHMyV9MQuBqCGZk49nURrndA4GoKzM03TjfXj37dtUJv675Qexi8YpX2fGjtnyPxwKOad56/x60f4I6FsMr1PXA9FbqX6u1Wob9r64A+f6JVf/dk+/HRryedYZ6G61qcqfwajt4LtU/A4bfqa79+aYvElYPVEg+m9RQ39JjWdyzarrnaCm1XYnFSawrial2jI+F670ed2m/fr4sh732/L2fDvna/k399oi59r4Z5ufTGDr2t7ll9n+++fK3EH7tcz+mXLjsg8cVzDkpcl9C5/zUJ3feHh+olbh+ukfjAwDUSb2nVPgZ1X9HzSuOm3Ho40aA+V7FwhQAAADAgAAAAJZQyyJ92ZmbWoyvI2nV1eyQuC/RS34Ov0MtFfXv1AaoOaorg2FWaknjvBfdJvLasTeLI9HLe9nKd8pLs19sTJ/tGv077y+kz8FJgPKBTAS0cfwljc62Mk8dy96/bp+mFobl6Cf7/BnX6TvkJTQEsekTzR2Fnn8R+qlNQpemqqEZTBGU9ev/q3bp9Tx/UNtpLN+nvFh3PLdM65hLyBEs9z3a9i3Tf/tWFv5X4hpqdEh9Ia0rgI4++ReLmPZquSx1xS+Qe18vSmQa9zBuXuyVwXcogUeZaK/tL5HnxmPTCiJ9qNkFqcSacR9w0zdilUzNdeefObj2Hh63tEi96yi3Bfq+e47vnaBr417V6SX9gkZ4Hhqvd38vurZoc1Ne/okP338IdmpKIOtxU+BE9T5QCrhAAAAAGBAAAgAEBAACwUqohcLnUcETjHf2a77mmVmsK3rbqcYmf/JBOOakr02kd72nYIvHVldrm0o+Udo5oXvnrD71S4jVu+crM4bwWp7OgnWns8pmBn07U46b+BfoKh3n509TjmvNfvktf+7jatUEecHNOBzWOR7SGI56gLWjgbq8a0lzfOW36/Iljutxy3KO5zii/vuJMp5zOhqlpeSo69fc7NKTtaytrdV/1ZFw9yKCe4pJuDdpw2L033b729Sd+2mFUq7E/b8SVmtcOBnKPHw7oOckfGWNqCmbidGVvvOM5dvUGvv7A5eSjPlc7pDPHx0i591bK38Gds3z7dW/MsurT4L3KFQIAAMCAAAAAMCAAAABWQjUEGbc88bKf6hzdexqvkvjE5dqidFWV9g24rekJiZuT+ngLEprnPeJanP6q9wKJv3r/jRKf9586Bzbap3NO47TrPTDDxUOat8+4/KfPt8Uuvxbl51PdY5lrZzr2yV1u1eX6xvREcPUNQcLd39UgWK/mIoPD+vP+d/Xb43/Xcc3GdrZOlLf/5/6uRW57uOESie+76CKJK9q0b0GTrlpu1fs69BttGvvjOHbnpcC13PZZ5Dij+94vpZ3fA2PMcTFem2OzWbHvi2qi13dMDcMUbkuRcIUAAAAwIAAAAAwIAACAlVANgc/dBU/tlvi8T+gSswcXaJ+BvQvXSdy9TPvfDzRpti/ppqjO36o9zcsP6tzy1ce2Spzp16UyMTnj1hSkpzh36vLy8fhtCSZvMts70VoGszFvnPc7p48ek5ua/kP7hTT5epExj+V644/zXC+Kf9tP5VoUs3Hfo6i4QgAAABgQAAAABgQAAMBKqIbA8/3mMyfc/GEXJ3VZdNNV0SfvbKeVZ50x65y7XKvP/ebneqc6d1pKudlS2pbpwL9ecZHXCWH/YQbhCgEAAGBAAAAAGBAAAAAr4RoCzDCllvsFAAiuEAAAAAYEAACAAQEAADAGBAAAwBgQAAAAY0AAAACMAQEAADCzIKYXNwAAsx5XCAAAAAMCAADAgAAAABgDAgAAYAwIAACAMSAAAABm9v+SZM2NlZjEPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a few samples\n",
    "N = 16\n",
    "z = torch.randn((N, d)).to(device)\n",
    "sample = model.decoder(z)\n",
    "display_images(None, sample, N // 4, count=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2ad8fa-ae8c-4d5d-aa2a-fdbe84186eaa",
   "metadata": {
    "canvas": {
     "comments": [],
     "componentType": "CodeCell",
     "copiedOriginId": null,
     "hasOpendPDC": false,
     "headerColor": 0,
     "id": "9609e541-cef9-423a-a362-6ccb80d11989",
     "isComponent": false,
     "meta_id": "b4141684-0a2b-469b-ba55-8a2756d008f5",
     "name": "",
     "parents": [],
     "preDefined": null
    }
   },
   "source": [
    "### Visualize with interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "453833c6-d3a5-4f96-91d7-10aa02c2e6d7",
   "metadata": {
    "canvas": {
     "comments": [],
     "componentType": "CodeCell",
     "copiedOriginId": null,
     "hasOpendPDC": false,
     "headerColor": 5,
     "id": "1c15b12d-e85a-4342-876e-80103630540d",
     "isComponent": true,
     "meta_id": "453833c6-d3a5-4f96-91d7-10aa02c2e6d7",
     "name": "Interpolation start-end point",
     "parents": [
      {
       "id": "73fb295b-17e9-4ca6-a42f-02720231688c",
       "name": "Train VAE"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose starting and ending point for the interpolation -> shows original and reconstructed\n",
    "x, _ = next(iter(train_loader))\n",
    "_, mu, _ = model(x.to(device))\n",
    "mu = mu.detach().requires_grad_(False)\n",
    "\n",
    "A, B = 0, -1\n",
    "sample = model.decoder(torch.stack((mu[A].data, mu[B].data), 0)).detach()\n",
    "display_images(None, torch.stack(((x[A].data.view(-1), x[B].data.view(-1), sample.data[0], sample.data[1])), 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5c377d1-936e-4242-87af-d1cbf4284f29",
   "metadata": {
    "canvas": {
     "comments": [],
     "componentType": "CodeCell",
     "copiedOriginId": null,
     "hasOpendPDC": false,
     "headerColor": 5,
     "id": "c8c3d975-8f3a-4563-aec3-011fef22b19a",
     "isComponent": true,
     "meta_id": "c5c377d1-936e-4242-87af-d1cbf4284f29",
     "name": "Visualize interpolation",
     "parents": [
      {
       "id": "1c15b12d-e85a-4342-876e-80103630540d",
       "name": "Interpolation start-end point"
      }
     ],
     "preDefined": null
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform an interpolation between input A and B, in N steps\n",
    "N = 16\n",
    "code = torch.Tensor(N, 20).to(device)\n",
    "sample = torch.Tensor(N, 28, 28).to(device)\n",
    "for i in range(N):\n",
    "    code[i] = i / (N - 1) * mu[B].data + (1 - i / (N - 1)) * mu[A].data\n",
    "sample = model.decoder(code)\n",
    "display_images(None, sample, N // 4, count=True)"
   ]
  }
 ],
 "metadata": {
  "canvas": {
   "parameters": [
    {
     "name": "batch_size",
     "type": "int",
     "value": "256"
    },
    {
     "name": "d",
     "type": "int",
     "value": "20"
    },
    {
     "name": "epochs",
     "type": "int",
     "value": "8"
    },
    {
     "name": "beta",
     "type": "float",
     "value": "1.0"
    },
    {
     "name": "learning_rate",
     "type": "float",
     "value": "1e-3"
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (Link)",
   "language": "python",
   "name": "mrx_link"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}