{
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  {
   "cell_type": "markdown",
   "id": "5e6a56d2",
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   "source": [
    "# Exercise Sheet No. 11\n",
    "\n",
    "---\n",
    "\n",
    "> Machine Learning for Natural Sciences, Summer 2024, Jun.-Prof. Pascal Friederich, pascal.friederich@kit.edu\n",
    "> \n",
    "> Deadline: 08.07.2024, 8 am\n",
    "> \n",
    "> Tutor: luca.torresi@kit.edu  \n",
    "> **Please ask questions in the forum and only contact the Tutor when there are issues with the grading**\n",
    "\n",
    "---\n",
    "**Topic**: This exercise sheet will introduce you to generative models in machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b3a493",
   "metadata": {},
   "source": [
    "Please add here your group members' names and student IDs. \n",
    "\n",
    "Names: Nils Lennart Bruns\n",
    "\n",
    "IDs: 2460137\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b067d4",
   "metadata": {
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    "editable": false,
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     "cell_type": "markdown",
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   },
   "source": [
    "# Variational AutoEncoders\n",
    "\n",
    "In this exercise we will work on Variational AutoEncoders, first introduced by Welling and Kingma in a popular paper published in 2014 (https://arxiv.org/pdf/1312.6114.pdf).\n",
    "We will implement a vanilla version of VAE, with encoder and decoder composed of fully connected layers only, and try to use it for an anomaly detection task. We will then see how we can impose nice properties on the latent representation of data produced by the network simply augmenting it with a linear regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8aaceed3",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "15d05daa3272078a4f2f2606557315ae",
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     "grade_id": "cell-99005ee5eaa17570",
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    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "import os\n",
    "import tempfile\n",
    "import requests\n",
    "import importlib\n",
    "import importlib.util\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from collections import Counter\n",
    "\n",
    "from typing import Optional\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "def nextcloud_download(url: str, raw: bool = False) -> str:\n",
    "    \"\"\"\n",
    "    Downloads the content of a file from a nextcloud server and returns \n",
    "    it eithers as a string or a bytes object if the ``raw`` flag is set.\n",
    "    \"\"\"\n",
    "    response = requests.get(f'{url}/download')\n",
    "    content = response.content\n",
    "    if not raw:\n",
    "        content = content.decode('utf-8')\n",
    "    \n",
    "    return content\n",
    "\n",
    "\n",
    "def nextcloud_import(url: str, name: str = 'module') -> 'Module':\n",
    "    \"\"\"\n",
    "    Downloads the content of a python module file from a nextcloud server \n",
    "    and returns the imported module instance.\n",
    "    \"\"\"\n",
    "    with tempfile.TemporaryDirectory() as path:\n",
    "        file_path = os.path.join(path, f'{name}.py')\n",
    "        with open(file_path, 'w') as file:\n",
    "            content = nextcloud_download(url)\n",
    "            file.write(content)\n",
    "        \n",
    "        spec = importlib.util.spec_from_file_location(name, file_path)\n",
    "        module = importlib.util.module_from_spec(spec)\n",
    "        spec.loader.exec_module(module)\n",
    "        \n",
    "        return module\n",
    "\n",
    "utils = nextcloud_import('https://bwsyncandshare.kit.edu/s/6XgH268f2DmiFGA')\n",
    "get_data, AbstractTrainer, plot_reconstr = utils.get_data, utils.AbstractTrainer, utils.plot_reconstr\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aae044b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# if you are not working on colab you can uncomment the line below \n",
    "# to have interactive plots, on colab it doesn't work\n",
    "#%matplotlib notebook   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf591d7",
   "metadata": {
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    "editable": false,
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     "cell_type": "markdown",
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   },
   "source": [
    "Let's take a look at the data we will work on.\n",
    "\n",
    "The dataset consists of ECG segments of healthy patients and patients suffering from premature ventricular contraction (PVC). Segments were padded or cut to measure 350 time steps each.\n",
    "\n",
    "In the first part of the exercise we will implement a detector for anomalous hearbeats, thus our training and validation sets will consist of normal heartbeats only while in the test set we will have both normal and PVC ECGs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "490e9dd6",
   "metadata": {
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     "cell_type": "code",
     "checksum": "c86b3911854686e161c3cadbef969815",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of \n",
      "\t train set : 6585 data points \n",
      "\t validation set : 997 data points \n",
      "\t test set : 1275 data points\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "np.random.seed(101)\n",
    "train, val, test = get_data(batch_size=1)\n",
    "print(f'Dimension of \\n\\t train set : {len(train)} data points \\n\\t validation set : {len(val)} data points \\n\\t test set : {len(test)} data points')\n",
    "counts = Counter()\n",
    "fig, axs = plt.subplots(nrows=2, ncols=3, sharex=True)\n",
    "for batch in test:\n",
    "    if sum(counts.values())==6:\n",
    "            break\n",
    "    data = batch['data']\n",
    "    label = batch['labels']\n",
    "    if label==0 and counts[0]<3:\n",
    "        axs[0, counts[0]].plot(np.arange(350), data.squeeze())\n",
    "        counts[0] += 1\n",
    "    if label==1 and counts[1]<3:\n",
    "        axs[1, counts[1]].plot(np.arange(350), data.squeeze())\n",
    "        counts[1] += 1\n",
    "axs[0,0].set_ylabel('Normal')\n",
    "axs[1,0].set_ylabel('PVC')\n",
    "fig.tight_layout()\n",
    "fig.show()\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6a0b02",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "cdccaf5b275a91ac3be970645bf51074",
     "grade": false,
     "grade_id": "cell-d4aeeb6e41141a77",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "An autoencoder is an unsupervised representation learning algorithm based on neural networks that is used to learn efficient codings of unlabeled data. In particular, by imposing a bottleneck in the encoding layer of the network we can extract a compressed representation of the original input leveraging the structure present in it, i.e. the correlations among input features. \n",
    "\n",
    "In an AE input data is converted into a deterministic encoding vector, i.e. for each of the latent features the encoder outputs a single value. VAEs are an extension of AE int the sense that instead of performing a single value estimation of the latent vector they produce a probabilistic distribution $q_{\\phi}(z|x)$ in the latent space. This is done leveraging variational bayesian inference: an approximation of the distribution is picked from some tractable family and then this is made as close as possible to the true posterior. In particular in the original formulation of VAE the prior imposed on the latent representation is a standard Gaussian and the mean field approximation is used, i.e. the latent variables are considered as independent from each other.\n",
    "\n",
    "Being the latent representation of our data a probabilistic distribution, to reconstruct input data we have to sample points from it $z$~$q_{\\phi}(z|x)$. The fact that one of the nodes in the computational graph is a random node would create a problem in the backpropagation step because the gradient would not be able to 'flow' through it.\n",
    "\n",
    "One of the key elements in the VAE architecture is the so called 'reparameterization trick', that allow us to express a distribution $q_{\\phi}(z|x)$ as a two-step generative process:\n",
    "1. sample a noise variable $\\epsilon$ from a simple distribution:\n",
    "2. apply a deterministic transformation $g_{\\phi}(\\epsilon,x)$ that maps the random noise into a more complex distribution.\n",
    "\n",
    "\n",
    "In the case of Gaussian distributions the reparametrization trick is particularly simple: instead of writing $z$∼$q_{\\phi}(z|x)=\\mathcal{N}(\\mu_{\\phi}, \\Sigma_{\\phi})$ we can just write $z=g_{\\phi}(\\epsilon, x)= \\mu_{\\phi} +  \\sigma_{\\phi}*\\epsilon$,  where $\\epsilon$ is a vector of iid variables $\\epsilon_{i}$∼$\\mathcal{N}(0,1)$ and $*$ is the Hadamard product, and then backpropagate the gradient through $\\mu_{\\phi}$ and $\\sigma_{\\phi}$, as shown in the following picture:\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "<img src=\"https://bwsyncandshare.kit.edu/s/tREreFEeLmL5ecz/download\" style=\"width: 500px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059c52f9",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "9572bbb0be3b16da18de453cf0246309",
     "grade": false,
     "grade_id": "cell-18abb1cd4791623c",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "In the following a partial implementation of a simple VAE with MLPs as encoder and decoder is provided. Complete the functions, paying attention to the fact that the encoder has to produce the logarithm of the standard deviation of each of the latent variables (this is done to ensure that the standard deviation is always positive).\n",
    "\n",
    "Hint: torch has a function similar to numpy.random.randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe726aa2",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "f40c49055563de6414f9b1e1bf2f01dc",
     "grade": false,
     "grade_id": "VAE_class",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "class VAE(nn.Module):\n",
    "    def __init__(self, input_dim : int = 350, embedding_dim : int = 30):\n",
    "        super().__init__()\n",
    "        self.embedding_dim = embedding_dim\n",
    "\n",
    "        self._backbone = nn.Sequential(nn.Linear(input_dim, 500),\n",
    "                    nn.LeakyReLU(negative_slope=0.2),\n",
    "                    nn.Dropout(0.4)\n",
    "                    )\n",
    "        self._fc_mean = nn.Linear(500, embedding_dim)\n",
    "        self._fc_logstd = nn.Sequential(nn.Linear(500, embedding_dim),\n",
    "                                    nn.LeakyReLU(negative_slope=0.05)\n",
    "                                   )\n",
    "        \n",
    "        self._decoder = nn.Sequential(nn.Linear(embedding_dim, 500),\n",
    "                    nn.LeakyReLU(negative_slope=0.2),\n",
    "                    nn.Dropout(0.4),\n",
    "                    nn.Linear(500, input_dim)\n",
    "                    )\n",
    "\n",
    "    def encode(self, x):\n",
    "        '''\n",
    "        The encoder is composed of a first fully connected layer followed by \n",
    "        two parallel linear layers that generate independently the mean and logstd of the latent distribution\n",
    "        '''\n",
    "        temp = None\n",
    "        mean, logstd = None, None\n",
    "\n",
    "        temp = self._backbone(x)\n",
    "        mean, logstd = self._fc_mean(temp), self._fc_logstd(temp)\n",
    "        \n",
    "        return mean, logstd\n",
    "        \n",
    "    def reparameterize(self, mean, logstd): \n",
    "        '''\n",
    "        Once the latent distribution is computed from the input a latent vector has to be sampled from it.\n",
    "        We are working on gaussians, look at the formula in the block above \n",
    "        and remember that the encoder produces the logstd.\n",
    "        '''\n",
    "        std = None\n",
    "        eps = None\n",
    "        z = None\n",
    "\n",
    "        std = torch.exp(logstd)\n",
    "        eps = torch.normal(0, 1, logstd.shape)\n",
    "        z = mean + eps*std\n",
    "        return z\n",
    "    \n",
    "    def decode(self, z):\n",
    "        return self._decoder(z)\n",
    "\n",
    "    def forward(self, x):\n",
    "        '''\n",
    "        VAE computes the latent distribution associated with the input, \n",
    "        samples a vector from it and finally decodes it.\n",
    "        '''\n",
    "        mean, logstd = None, None\n",
    "        z = None\n",
    "        out = None \n",
    "\n",
    "        mean, logstd = self.encode(x)\n",
    "        z = self.reparameterize(mean, logstd)\n",
    "        out = self.decode(z)\n",
    "        \n",
    "        return [out, mean, logstd, z]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb1b8399",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "356112089200a9fdef51d2cd5c58e49d",
     "grade": true,
     "grade_id": "encode",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "# ID: encode - possible points: 2\n",
    "\n",
    "vae = VAE(1,2)\n",
    "m, s = vae.encode(torch.Tensor([1]))\n",
    "assert m.shape==s.shape and m.shape==torch.Size([2]), 'error in encodings dimension'\n",
    "\n",
    "# other hidden tests\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "822602be",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "774d1f0bcbd04f75add1e4c3b4dcaed2",
     "grade": true,
     "grade_id": "reparameterize",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "# ID: reparameterize - possible points: 1\n",
    "\n",
    "vae = VAE(1,2)\n",
    "s = vae.reparameterize(torch.Tensor([0,0]), torch.log(torch.tensor([1,1])))\n",
    "assert s.size()==torch.Size([2]), 'error in dimension of generated random vector'\n",
    "\n",
    "# hidden MonteCarlo test to check that reparameterize(m, s) produces samples from a normal distribution N(m,s)\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de21c6f6",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "eb08d3b2350ccefb2c66f718723ab3eb",
     "grade": true,
     "grade_id": "forward",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input and output have same dimension: True\n",
      "latent vector is 8-dimensional: True\n"
     ]
    }
   ],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "# ID: forward - possible points: 1\n",
    "\n",
    "in_dim, emb_dim = torch.randint(1, 10, (2,))\n",
    "vae = VAE(in_dim, emb_dim)\n",
    "inp = torch.randn(in_dim)\n",
    "out, _, _, z = vae.forward(inp)\n",
    "print(f'input and output have same dimension: {inp.shape==out.shape}')\n",
    "print(f'latent vector is {emb_dim}-dimensional: {z.shape==torch.Size([emb_dim])}')\n",
    "\n",
    "# similar hidden tests...\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05710adc",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "c0b521209be9ace68170f4c922339bc3",
     "grade": false,
     "grade_id": "cell-ce455d89657ee247",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "What we want to maximize in variational Bayes methods is the ELBO, which stands for \"evidence lower bound\":\n",
    "\n",
    "$\n",
    "\\begin{align}\n",
    "ELBO(\\theta, \\phi) = [\\mathbb{E}_{q_{\\phi}(z)}[\\log p_{\\theta}(x, z) - \\log q_{\\phi}(z|x)] ] = \\\\\n",
    "-KL[q_{\\phi}(z|x)|| p_{\\theta}(z)] + \\frac{1}{L} \\sum_{l=1}^{L} \\log p_{\\theta}(x|z^{(l)})\n",
    "\\end{align}\n",
    "$\n",
    "\n",
    "where $q_{\\phi}(z|x)$ represents the encoder and $\\log p_{\\theta}(x|z^{(l)})$ is the loglikelihood of the reconstruction and depends on the decoder parameters. (Loglikelihood is a fancy term which in our case represent simply the reconstruction error. Take a look at Bayesian interpretations of loss functions (MSE is the easiest) if you are interested)\n",
    "\n",
    "The KL (Kullback–Leibler) divergence is a measure of how similar two probability distributions are (if equal -> KL=0) and it is also known as relative entropy. Note that the KL divergence is not a distance metric since it is not symmetric.\n",
    "\n",
    "The KL divergence of some families of distributions can be computed analytically and that's the case if we choose Gaussians priors and posteriors.\n",
    "\n",
    "Implement the KL divergence between a Gaussian posterior $q_{\\phi}(z|x) = \\mathcal{N}(\\mu, \\Sigma)$, where $\\Sigma = diag(\\sigma_{j})$ with $j \\in {1, ..., J}$, and a standard Gaussian prior $p(z) = \\mathcal{N}(0, I)$ : &emsp; $-\\frac{1}{2} \\sum_{j=1}^{J} (1 + \\log((\\sigma_{j})^{2}) - (\\mu_{j})^{2} - (\\sigma_{j})^2)$\n",
    "\n",
    "<br><br><br>\n",
    "\n",
    "<img src=\"https://bwsyncandshare.kit.edu/s/2QwqARArsMtEQn7/download\" style=\"width: 500px;\">\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "214e88cf",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "aaea7093aeb48b2c638ec07f6f56560c",
     "grade": false,
     "grade_id": "KL",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def KL_normnorm(mean, logstd):\n",
    "    '''\n",
    "    Implement the closed form of KL divergence for a gaussian N(mean, std) as posterior \n",
    "    and a standard gaussian N(0, 1) as prior. Careful with the logarithm!\n",
    "    '''\n",
    "    kl = None\n",
    "\n",
    "    kl = torch.sum(-0.5*(1+2*logstd-mean.pow(2)-torch.exp(2*logstd)))\n",
    "        \n",
    "    return kl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80c60fc7",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "4bac745f9764271ab7240115f4b81184",
     "grade": true,
     "grade_id": "kl_divergence",
     "locked": true,
     "points": 3,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "# ID: kl_divergence - possible points: 3\n",
    "\n",
    "mean = torch.randn(3)\n",
    "logstd = abs(torch.randn_like(mean))\n",
    "try:\n",
    "    KL_normnorm(mean, logstd).item()\n",
    "except:\n",
    "    print('KL divergence of two probability distributions is a number, not a vector...')\n",
    "\n",
    "# hidden test that checks if the KL_normnorm gives correct values\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75da9ec",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "c809acb227cbd2e3db959c8b5c6fb02b",
     "grade": false,
     "grade_id": "cell-aed77beda024b9ab",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Differently from the original formulation of VAE we are going to multiply the KL divergence with a scalar parameter in such a way to regulate the tradeoff between the two terms of the ELBO loss (this is somehow similar to beta-VAE: https://openreview.net/references/pdf?id=Sy2fzU9gl). Then, as suggested in https://arxiv.org/pdf/1511.06349.pdf, we will initially set this parameter to zero and increase it linearly w.r.t. the number of epochs (this approach is termed KL warmup)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14194fa9",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "08e9a24abfff5d0a793d1394c3984f93",
     "grade": false,
     "grade_id": "cell-2a70a73e22bf68ab",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "class VAE_Trainer(AbstractTrainer):\n",
    "    def __init__(self, model : nn.Module, \n",
    "                 train_loader : Optional[DataLoader] = None, \n",
    "                 val_loader : Optional[DataLoader] = None,\n",
    "                 test_loader : Optional[DataLoader] = None, \n",
    "                 optimizer = None):\n",
    "        super().__init__(model=model,\n",
    "                         train_loader = train_loader, \n",
    "                         val_loader = val_loader, \n",
    "                         test_loader = test_loader, \n",
    "                         optimizer = optimizer,\n",
    "                         kl_scaling = 0.1)\n",
    "        self.measures = {'elbo' : [], 'reconstr' : [], 'kl': []}\n",
    "\n",
    "    def _loss(self, target, label, *args):\n",
    "        out = args[0] \n",
    "        mean = args[1]\n",
    "        logstd = args[2]\n",
    "        batch_dim = target.shape[0]\n",
    "        # loglikelihood\n",
    "        reconstr_error = self.reconstr_loss(out, target).sum()/batch_dim\n",
    "        # KL divergence\n",
    "        kl = KL_normnorm(mean, logstd)/batch_dim\n",
    "        # combine loglikelihood and KL divergence in the ELBO loss\n",
    "        ELBO = reconstr_error + kl * self.kl_scaling * (self.ep/self.epochs) # kl * scaling * warmup_term\n",
    "        return [ELBO, reconstr_error, kl]\n",
    "\n",
    "    def test(self):\n",
    "        self.model.eval()\n",
    "        reconstr_errors = []\n",
    "        with torch.no_grad():\n",
    "            for batch in self.train_loader:\n",
    "                data = batch['data'].to(self.device)\n",
    "                out, mean, logstd, z = self.model(data)\n",
    "                reconstr_error = self.reconstr_loss(out, data).sum(-1)\n",
    "                reconstr_errors.append(reconstr_error)\n",
    "        reconstr_errors = torch.cat(reconstr_errors)\n",
    "        # set the threshold on the 0.85 quantile of the reconstruction error on the train set (normal behaviour)\n",
    "        thresh = torch.quantile(reconstr_errors, 0.85)\n",
    "        print(f'reconstr_errors avg : {reconstr_errors.mean()}, thresh : {thresh}')\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.hist(reconstr_errors.numpy(), bins=np.arange(0, 15, 0.125))\n",
    "        ax.set_title('train')\n",
    "        ax.axvline(x=thresh, c='red')\n",
    "        fig.show()\n",
    "\n",
    "        reconstr_errors = []\n",
    "        labels = []\n",
    "        with torch.no_grad():\n",
    "            for batch in self.test_loader:\n",
    "                data = batch['data'].to(self.device)\n",
    "                label = batch['labels'].to(self.device).squeeze()\n",
    "                out, mean, logstd, z = self.model(data)\n",
    "                reconstr_error = self.reconstr_loss(out, data).sum(-1)\n",
    "                reconstr_errors.append(reconstr_error)\n",
    "                labels.append(label)\n",
    "        reconstr_errors = torch.cat(reconstr_errors)\n",
    "        labels = torch.cat(labels)\n",
    "\n",
    "        outliers_error = reconstr_errors[labels==1]\n",
    "        normal_error = reconstr_errors[labels==0]\n",
    "        fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True)\n",
    "        ax1.hist(normal_error.numpy(), bins=np.arange(0, 15, 0.125))\n",
    "        ax1.set_title('normal')\n",
    "        ax1.axvline(x=thresh, c='red')\n",
    "        ax2.hist(outliers_error.numpy(), bins=np.arange(0, 15, 0.125))\n",
    "        ax2.set_title('anomalies')\n",
    "        ax2.axvline(x=thresh, c='red')\n",
    "        fig.show()\n",
    "        print(f' avg recontruction error \\n \\t normal data : {normal_error.mean()}, anomalies : {outliers_error.mean()}')\n",
    "        \n",
    "        fig, ax = plt.subplots()\n",
    "        preds = (reconstr_errors>thresh).float()\n",
    "        cm = confusion_matrix(labels.numpy(), preds.numpy(), normalize='true')\n",
    "        sb.heatmap(cm, square=True, xticklabels=False, yticklabels=False, \n",
    "                    annot=True, cbar=False, cmap=\"Reds\", fmt='.2f').get_figure()\n",
    "        ax.set_title('Anomaly detection: confusion matrix')\n",
    "        fig.show()\n",
    "        \n",
    "        ##compute precision, recall and f1-score\n",
    "        # precision = true_positive/(true_positive + false_positive)\n",
    "        # recall = true_positive/(true_positive + false_negative)\n",
    "        true = labels==1            \n",
    "        tp = preds[true].sum()      # true_positive\n",
    "        pos = (preds==1).sum()      # (true_positive + false_positive)\n",
    "        true = true.sum()           # (true_positive + false_negative)\n",
    "        \n",
    "        precision = tp/pos\n",
    "        recall = tp/true\n",
    "        F1_score = 2*(precision * recall)/(precision + recall)\n",
    "        \n",
    "        print(f'Quality measures of our classifier:\\n\\t precision : {precision}\\n\\t recall : {recall}\\n\\t F1_score : {F1_score}')\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c17e447",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "c1ae748e48c43c5a1dfba0a673f979d0",
     "grade": false,
     "grade_id": "cell-2b5deef761410602",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "We will now train our network on the 'normal' ECGs for a cycle of 100 epochs, without using any early stopping criteria, with a scaling constant of 0.1 for the KL divergence and using KL warmup. We will then use our VAE as anomaly detector on a test set composed of an equal (more or less) number of 'normal' and 'PVC' ECGs. \n",
    "\n",
    "To do so we will compute the distribution of the reconstruction errors over the training set, we will get the 0.85 (arbitrarily) quantile of the distribution and use it as a threshold to classify unseen data as belonging or not to the 'normal' class. \n",
    "\n",
    "Graphs produced are:\n",
    "1. summary of training\n",
    "2. few examples of reconstruction of test data\n",
    "3. distribution of reconstruction errors on training set and 0.85 quantile in red\n",
    "4. distribution of reconstruction errors on test set, separately for the 2 classes (threshold in red)\n",
    "5. confusion matrix associated to our detector\n",
    "\n",
    "Three measures were computed to evaluate our classifier: precision, recall and F1 score. Check the code above to see how these are computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "29f1b82e",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "08d445356d6dd3a4bad154de53a05a3f",
     "grade": false,
     "grade_id": "cell-09a5f3aba79593ed",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===> epoch: 0\n",
      "Train\telbo : 45.491065 reconstr : 45.491065 kl : 3.066583 \n",
      "Val\telbo : 12.970618 reconstr : 12.970618 kl : 13.377681 \n",
      "\n",
      "===> epoch: 1\n",
      "Train\telbo : 16.221464 reconstr : 16.203431 kl : 18.032871 \n",
      "Val\telbo : 8.674325 reconstr : 8.655612 kl : 18.71311 \n",
      "\n",
      "===> epoch: 2\n",
      "Train\telbo : 10.586143 reconstr : 10.536554 kl : 24.794579 \n",
      "Val\telbo : 7.462139 reconstr : 7.406952 kl : 27.593504 \n",
      "\n",
      "===> epoch: 3\n",
      "Train\telbo : 8.13624 reconstr : 8.027468 kl : 36.257566 \n",
      "Val\telbo : 6.477224 reconstr : 6.364802 kl : 37.473863 \n",
      "\n",
      "===> epoch: 4\n",
      "Train\telbo : 7.146991 reconstr : 6.977703 kl : 42.321978 \n",
      "Val\telbo : 6.107455 reconstr : 5.948937 kl : 39.629511 \n",
      "\n",
      "===> epoch: 5\n",
      "Train\telbo : 6.471424 reconstr : 6.254564 kl : 43.37206 \n",
      "Val\telbo : 5.64766 reconstr : 5.446481 kl : 40.235706 \n",
      "\n",
      "===> epoch: 6\n",
      "Train\telbo : 6.190407 reconstr : 5.927967 kl : 43.740077 \n",
      "Val\telbo : 5.471823 reconstr : 5.239916 kl : 38.651284 \n",
      "\n",
      "===> epoch: 7\n",
      "Train\telbo : 6.018186 reconstr : 5.721383 kl : 42.400379 \n",
      "Val\telbo : 5.369645 reconstr : 5.106821 kl : 37.54633 \n",
      "\n",
      "===> epoch: 8\n",
      "Train\telbo : 5.842622 reconstr : 5.52431 kl : 39.788995 \n",
      "Val\telbo : 5.13834 reconstr : 4.850354 kl : 35.998205 \n",
      "\n",
      "===> epoch: 9\n",
      "Train\telbo : 5.653349 reconstr : 5.304983 kl : 38.707355 \n",
      "Val\telbo : 4.862636 reconstr : 4.544077 kl : 35.395449 \n",
      "\n",
      "===> epoch: 10\n",
      "Train\telbo : 5.419146 reconstr : 5.04512 kl : 37.402652 \n",
      "Val\telbo : 4.753671 reconstr : 4.421857 kl : 33.181336 \n",
      "\n",
      "===> epoch: 11\n",
      "Train\telbo : 5.352678 reconstr : 4.954492 kl : 36.198744 \n",
      "Val\telbo : 4.586481 reconstr : 4.22786 kl : 32.601934 \n",
      "\n",
      "===> epoch: 12\n",
      "Train\telbo : 5.210181 reconstr : 4.793263 kl : 34.743177 \n",
      "Val\telbo : 4.478278 reconstr : 4.092132 kl : 32.178856 \n",
      "\n",
      "===> epoch: 13\n",
      "Train\telbo : 5.108193 reconstr : 4.662128 kl : 34.312679 \n",
      "Val\telbo : 4.566015 reconstr : 4.151427 kl : 31.891392 \n",
      "\n",
      "===> epoch: 14\n",
      "Train\telbo : 5.064629 reconstr : 4.591678 kl : 33.782238 \n",
      "Val\telbo : 4.426576 reconstr : 3.994764 kl : 30.843714 \n",
      "\n",
      "===> epoch: 15\n",
      "Train\telbo : 5.035077 reconstr : 4.54001 kl : 33.004463 \n",
      "Val\telbo : 4.29438 reconstr : 3.829374 kl : 31.000378 \n",
      "\n",
      "===> epoch: 16\n",
      "Train\telbo : 4.934575 reconstr : 4.412367 kl : 32.637999 \n",
      "Val\telbo : 4.239362 reconstr : 3.753532 kl : 30.364419 \n",
      "\n",
      "===> epoch: 17\n",
      "Train\telbo : 4.914443 reconstr : 4.363602 kl : 32.402386 \n",
      "Val\telbo : 4.201862 reconstr : 3.689053 kl : 30.165241 \n",
      "\n",
      "===> epoch: 18\n",
      "Train\telbo : 4.832721 reconstr : 4.263146 kl : 31.643052 \n",
      "Val\telbo : 4.156112 reconstr : 3.626472 kl : 29.424426 \n",
      "\n",
      "===> epoch: 19\n",
      "Train\telbo : 4.8657 reconstr : 4.275298 kl : 31.073788 \n",
      "Val\telbo : 4.156504 reconstr : 3.604713 kl : 29.041604 \n",
      "\n",
      "===> epoch: 20\n",
      "Train\telbo : 4.788054 reconstr : 4.17717 kl : 30.544212 \n",
      "Val\telbo : 4.134074 reconstr : 3.561809 kl : 28.613236 \n",
      "\n",
      "===> epoch: 21\n",
      "Train\telbo : 4.745809 reconstr : 4.111409 kl : 30.209502 \n",
      "Val\telbo : 4.052793 reconstr : 3.455973 kl : 28.419997 \n",
      "\n",
      "===> epoch: 22\n",
      "Train\telbo : 4.75856 reconstr : 4.101534 kl : 29.86483 \n",
      "Val\telbo : 4.132521 reconstr : 3.516252 kl : 28.012187 \n",
      "\n",
      "===> epoch: 23\n",
      "Train\telbo : 4.75285 reconstr : 4.078955 kl : 29.299762 \n",
      "Val\telbo : 3.949082 reconstr : 3.31303 kl : 27.654465 \n",
      "\n",
      "===> epoch: 24\n",
      "Train\telbo : 4.697874 reconstr : 4.005757 kl : 28.838189 \n",
      "Val\telbo : 3.946271 reconstr : 3.294826 kl : 27.143534 \n",
      "\n",
      "===> epoch: 25\n",
      "Train\telbo : 4.696168 reconstr : 3.990548 kl : 28.224797 \n",
      "Val\telbo : 3.9448 reconstr : 3.281075 kl : 26.548979 \n",
      "\n",
      "===> epoch: 26\n",
      "Train\telbo : 4.688014 reconstr : 3.968425 kl : 27.676505 \n",
      "Val\telbo : 3.937573 reconstr : 3.257242 kl : 26.166586 \n",
      "\n",
      "===> epoch: 27\n",
      "Train\telbo : 4.690273 reconstr : 3.953065 kl : 27.303989 \n",
      "Val\telbo : 4.054942 reconstr : 3.365429 kl : 25.537523 \n",
      "\n",
      "===> epoch: 28\n",
      "Train\telbo : 4.715365 reconstr : 3.97007 kl : 26.61771 \n",
      "Val\telbo : 3.96735 reconstr : 3.271107 kl : 24.865816 \n",
      "\n",
      "===> epoch: 29\n",
      "Train\telbo : 4.662474 reconstr : 3.911854 kl : 25.883449 \n",
      "Val\telbo : 3.937401 reconstr : 3.232012 kl : 24.323769 \n",
      "\n",
      "===> epoch: 30\n",
      "Train\telbo : 4.642672 reconstr : 3.880083 kl : 25.419621 \n",
      "Val\telbo : 3.88803 reconstr : 3.170136 kl : 23.929774 \n",
      "\n",
      "===> epoch: 31\n",
      "Train\telbo : 4.614855 reconstr : 3.844808 kl : 24.840239 \n",
      "Val\telbo : 3.91472 reconstr : 3.185002 kl : 23.539281 \n",
      "\n",
      "===> epoch: 32\n",
      "Train\telbo : 4.640095 reconstr : 3.858451 kl : 24.426391 \n",
      "Val\telbo : 3.90305 reconstr : 3.169176 kl : 22.933588 \n",
      "\n",
      "===> epoch: 33\n",
      "Train\telbo : 4.621006 reconstr : 3.833903 kl : 23.851598 \n",
      "Val\telbo : 3.898379 reconstr : 3.152529 kl : 22.601515 \n",
      "\n",
      "===> epoch: 34\n",
      "Train\telbo : 4.611088 reconstr : 3.81538 kl : 23.403175 \n",
      "Val\telbo : 3.910999 reconstr : 3.163794 kl : 21.976614 \n",
      "\n",
      "===> epoch: 35\n",
      "Train\telbo : 4.632172 reconstr : 3.832377 kl : 22.851298 \n",
      "Val\telbo : 3.931391 reconstr : 3.181966 kl : 21.412163 \n",
      "\n",
      "===> epoch: 36\n",
      "Train\telbo : 4.620236 reconstr : 3.81807 kl : 22.282372 \n",
      "Val\telbo : 3.908589 reconstr : 3.150042 kl : 21.070749 \n",
      "\n",
      "===> epoch: 37\n",
      "Train\telbo : 4.596288 reconstr : 3.790645 kl : 21.774134 \n",
      "Val\telbo : 3.845226 reconstr : 3.080936 kl : 20.656485 \n",
      "\n",
      "===> epoch: 38\n",
      "Train\telbo : 4.616102 reconstr : 3.805253 kl : 21.33814 \n",
      "Val\telbo : 3.924057 reconstr : 3.159124 kl : 20.129804 \n",
      "\n",
      "===> epoch: 39\n",
      "Train\telbo : 4.613481 reconstr : 3.801258 kl : 20.826225 \n",
      "Val\telbo : 3.871102 reconstr : 3.09972 kl : 19.779016 \n",
      "\n",
      "===> epoch: 40\n",
      "Train\telbo : 4.56588 reconstr : 3.746491 kl : 20.484726 \n",
      "Val\telbo : 3.826345 reconstr : 3.048966 kl : 19.434474 \n",
      "\n",
      "===> epoch: 41\n",
      "Train\telbo : 4.5627 reconstr : 3.737595 kl : 20.124524 \n",
      "Val\telbo : 3.822833 reconstr : 3.041683 kl : 19.052439 \n",
      "\n",
      "===> epoch: 42\n",
      "Train\telbo : 4.560984 reconstr : 3.733525 kl : 19.7014 \n",
      "Val\telbo : 3.863265 reconstr : 3.081521 kl : 18.612958 \n",
      "\n",
      "===> epoch: 43\n",
      "Train\telbo : 4.557354 reconstr : 3.726822 kl : 19.314703 \n",
      "Val\telbo : 3.845425 reconstr : 3.053631 kl : 18.413817 \n",
      "\n",
      "===> epoch: 44\n",
      "Train\telbo : 4.562183 reconstr : 3.726863 kl : 18.984566 \n",
      "Val\telbo : 3.830431 reconstr : 3.041741 kl : 17.92479 \n",
      "\n",
      "===> epoch: 45\n",
      "Train\telbo : 4.559648 reconstr : 3.723594 kl : 18.578981 \n",
      "Val\telbo : 3.974555 reconstr : 3.182054 kl : 17.61114 \n",
      "\n",
      "===> epoch: 46\n",
      "Train\telbo : 4.558557 reconstr : 3.718965 kl : 18.252014 \n",
      "Val\telbo : 3.863642 reconstr : 3.063894 kl : 17.38581 \n",
      "\n",
      "===> epoch: 47\n",
      "Train\telbo : 4.579377 reconstr : 3.734578 kl : 17.974455 \n",
      "Val\telbo : 3.920977 reconstr : 3.119764 kl : 17.047083 \n",
      "\n",
      "===> epoch: 48\n",
      "Train\telbo : 4.54489 reconstr : 3.694859 kl : 17.708986 \n",
      "Val\telbo : 3.899196 reconstr : 3.088433 kl : 16.890889 \n",
      "\n",
      "===> epoch: 49\n",
      "Train\telbo : 4.550559 reconstr : 3.692299 kl : 17.515509 \n",
      "Val\telbo : 3.820048 reconstr : 3.004565 kl : 16.642509 \n",
      "\n",
      "===> epoch: 50\n",
      "Train\telbo : 4.538084 reconstr : 3.673434 kl : 17.293006 \n",
      "Val\telbo : 3.875675 reconstr : 3.057479 kl : 16.36391 \n",
      "\n",
      "===> epoch: 51\n",
      "Train\telbo : 4.535058 reconstr : 3.666646 kl : 17.027688 \n",
      "Val\telbo : 3.882676 reconstr : 3.057995 kl : 16.17021 \n",
      "\n",
      "===> epoch: 52\n",
      "Train\telbo : 4.516569 reconstr : 3.641905 kl : 16.820456 \n",
      "Val\telbo : 3.831276 reconstr : 2.992415 kl : 16.131935 \n",
      "\n",
      "===> epoch: 53\n",
      "Train\telbo : 4.514316 reconstr : 3.628386 kl : 16.715656 \n",
      "Val\telbo : 3.842321 reconstr : 3.000424 kl : 15.884845 \n",
      "\n",
      "===> epoch: 54\n",
      "Train\telbo : 4.534768 reconstr : 3.644313 kl : 16.489902 \n",
      "Val\telbo : 3.880199 reconstr : 3.033611 kl : 15.677557 \n",
      "\n",
      "===> epoch: 55\n",
      "Train\telbo : 4.519895 reconstr : 3.620497 kl : 16.352689 \n",
      "Val\telbo : 3.857888 reconstr : 3.003658 kl : 15.531447 \n",
      "\n",
      "===> epoch: 56\n",
      "Train\telbo : 4.529994 reconstr : 3.625867 kl : 16.145119 \n",
      "Val\telbo : 3.839893 reconstr : 2.976467 kl : 15.418324 \n",
      "\n",
      "===> epoch: 57\n",
      "Train\telbo : 4.550016 reconstr : 3.635262 kl : 16.048314 \n",
      "Val\telbo : 3.876576 reconstr : 3.002827 kl : 15.328941 \n",
      "\n",
      "===> epoch: 58\n",
      "Train\telbo : 4.552659 reconstr : 3.628188 kl : 15.939142 \n",
      "Val\telbo : 3.870896 reconstr : 2.990101 kl : 15.186122 \n",
      "\n",
      "===> epoch: 59\n",
      "Train\telbo : 4.552542 reconstr : 3.623441 kl : 15.747471 \n",
      "Val\telbo : 3.904786 reconstr : 3.022788 kl : 14.949113 \n",
      "\n",
      "===> epoch: 60\n",
      "Train\telbo : 4.557371 reconstr : 3.619698 kl : 15.627879 \n",
      "Val\telbo : 3.828447 reconstr : 2.93474 kl : 14.895118 \n",
      "\n",
      "===> epoch: 61\n",
      "Train\telbo : 4.535573 reconstr : 3.590832 kl : 15.487545 \n",
      "Val\telbo : 3.859757 reconstr : 2.957645 kl : 14.788735 \n",
      "\n",
      "===> epoch: 62\n",
      "Train\telbo : 4.529363 reconstr : 3.575196 kl : 15.389782 \n",
      "Val\telbo : 3.840507 reconstr : 2.92936 kl : 14.695919 \n",
      "\n",
      "===> epoch: 63\n",
      "Train\telbo : 4.523139 reconstr : 3.564236 kl : 15.220687 \n",
      "Val\telbo : 3.811744 reconstr : 2.89417 kl : 14.564663 \n",
      "\n",
      "===> epoch: 64\n",
      "Train\telbo : 4.572166 reconstr : 3.599509 kl : 15.197769 \n",
      "Val\telbo : 3.895578 reconstr : 2.965961 kl : 14.525263 \n",
      "\n",
      "===> epoch: 65\n",
      "Train\telbo : 4.550036 reconstr : 3.570053 kl : 15.076663 \n",
      "Val\telbo : 3.873604 reconstr : 2.939829 kl : 14.365776 \n",
      "\n",
      "===> epoch: 66\n",
      "Train\telbo : 4.55072 reconstr : 3.572007 kl : 14.82898 \n",
      "Val\telbo : 3.936495 reconstr : 2.996521 kl : 14.242021 \n",
      "\n",
      "===> epoch: 67\n",
      "Train\telbo : 4.548074 reconstr : 3.553851 kl : 14.839154 \n",
      "Val\telbo : 3.888712 reconstr : 2.939808 kl : 14.162746 \n",
      "\n",
      "===> epoch: 68\n",
      "Train\telbo : 4.590375 reconstr : 3.592279 kl : 14.677882 \n",
      "Val\telbo : 3.897757 reconstr : 2.938551 kl : 14.105972 \n",
      "\n",
      "===> epoch: 69\n",
      "Train\telbo : 4.613248 reconstr : 3.601364 kl : 14.664982 \n",
      "Val\telbo : 3.947291 reconstr : 2.978004 kl : 14.047648 \n",
      "\n",
      "===> epoch: 70\n",
      "Train\telbo : 4.59717 reconstr : 3.577143 kl : 14.571801 \n",
      "Val\telbo : 3.898541 reconstr : 2.922228 kl : 13.947334 \n",
      "\n",
      "===> epoch: 71\n",
      "Train\telbo : 4.56633 reconstr : 3.536085 kl : 14.510495 \n",
      "Val\telbo : 3.917122 reconstr : 2.929755 kl : 13.906576 \n",
      "\n",
      "===> epoch: 72\n",
      "Train\telbo : 4.619413 reconstr : 3.57987 kl : 14.438096 \n",
      "Val\telbo : 4.024565 reconstr : 3.029564 kl : 13.819461 \n",
      "\n",
      "===> epoch: 73\n",
      "Train\telbo : 4.602405 reconstr : 3.556568 kl : 14.326531 \n",
      "Val\telbo : 3.92299 reconstr : 2.918624 kl : 13.75844 \n",
      "\n",
      "===> epoch: 74\n",
      "Train\telbo : 4.618838 reconstr : 3.560059 kl : 14.30782 \n",
      "Val\telbo : 3.941732 reconstr : 2.925651 kl : 13.730819 \n",
      "\n",
      "===> epoch: 75\n",
      "Train\telbo : 4.615666 reconstr : 3.542286 kl : 14.311735 \n",
      "Val\telbo : 3.944341 reconstr : 2.925 kl : 13.591215 \n",
      "\n",
      "===> epoch: 76\n",
      "Train\telbo : 4.637133 reconstr : 3.563572 kl : 14.125809 \n",
      "Val\telbo : 3.969331 reconstr : 2.941325 kl : 13.526395 \n",
      "\n",
      "===> epoch: 77\n",
      "Train\telbo : 4.6263 reconstr : 3.548828 kl : 13.993134 \n",
      "Val\telbo : 4.018313 reconstr : 2.986749 kl : 13.396943 \n",
      "\n",
      "===> epoch: 78\n",
      "Train\telbo : 4.650556 reconstr : 3.565427 kl : 13.911915 \n",
      "Val\telbo : 3.993031 reconstr : 2.954688 kl : 13.312096 \n",
      "\n",
      "===> epoch: 79\n",
      "Train\telbo : 4.656614 reconstr : 3.562097 kl : 13.85464 \n",
      "Val\telbo : 4.025845 reconstr : 2.978912 kl : 13.25232 \n",
      "\n",
      "===> epoch: 80\n",
      "Train\telbo : 4.654969 reconstr : 3.554278 kl : 13.758633 \n",
      "Val\telbo : 3.970362 reconstr : 2.911353 kl : 13.237612 \n",
      "\n",
      "===> epoch: 81\n",
      "Train\telbo : 4.665141 reconstr : 3.555 kl : 13.705443 \n",
      "Val\telbo : 4.070486 reconstr : 3.006509 kl : 13.135523 \n",
      "\n",
      "===> epoch: 82\n",
      "Train\telbo : 4.659712 reconstr : 3.536768 kl : 13.694439 \n",
      "Val\telbo : 4.018575 reconstr : 2.945998 kl : 13.0802 \n",
      "\n",
      "===> epoch: 83\n",
      "Train\telbo : 4.665592 reconstr : 3.536447 kl : 13.604156 \n",
      "Val\telbo : 4.012649 reconstr : 2.932823 kl : 13.009946 \n",
      "\n",
      "===> epoch: 84\n",
      "Train\telbo : 4.694306 reconstr : 3.560256 kl : 13.500599 \n",
      "Val\telbo : 4.0747 reconstr : 2.986138 kl : 12.959071 \n",
      "\n",
      "===> epoch: 85\n",
      "Train\telbo : 4.703705 reconstr : 3.556488 kl : 13.496669 \n",
      "Val\telbo : 4.115781 reconstr : 3.01752 kl : 12.920713 \n",
      "\n",
      "===> epoch: 86\n",
      "Train\telbo : 4.704148 reconstr : 3.553432 kl : 13.380416 \n",
      "Val\telbo : 4.065902 reconstr : 2.959343 kl : 12.866964 \n",
      "\n",
      "===> epoch: 87\n",
      "Train\telbo : 4.735403 reconstr : 3.571475 kl : 13.378475 \n",
      "Val\telbo : 4.110412 reconstr : 2.996801 kl : 12.800129 \n",
      "\n",
      "===> epoch: 88\n",
      "Train\telbo : 4.734539 reconstr : 3.562201 kl : 13.322028 \n",
      "Val\telbo : 4.100109 reconstr : 2.974787 kl : 12.787747 \n",
      "\n",
      "===> epoch: 89\n",
      "Train\telbo : 4.745466 reconstr : 3.568174 kl : 13.228005 \n",
      "Val\telbo : 4.10393 reconstr : 2.975424 kl : 12.679851 \n",
      "\n",
      "===> epoch: 90\n",
      "Train\telbo : 4.732844 reconstr : 3.552071 kl : 13.119698 \n",
      "Val\telbo : 4.097821 reconstr : 2.964374 kl : 12.593851 \n",
      "\n",
      "===> epoch: 91\n",
      "Train\telbo : 4.741144 reconstr : 3.55152 kl : 13.072791 \n",
      "Val\telbo : 4.123754 reconstr : 2.983133 kl : 12.5343 \n",
      "\n",
      "===> epoch: 92\n",
      "Train\telbo : 4.7412 reconstr : 3.542918 kl : 13.024807 \n",
      "Val\telbo : 4.155498 reconstr : 3.010873 kl : 12.441576 \n",
      "\n",
      "===> epoch: 93\n",
      "Train\telbo : 4.73744 reconstr : 3.534026 kl : 12.939942 \n",
      "Val\telbo : 4.14585 reconstr : 2.992568 kl : 12.400875 \n",
      "\n",
      "===> epoch: 94\n",
      "Train\telbo : 4.788215 reconstr : 3.573736 kl : 12.919985 \n",
      "Val\telbo : 4.160538 reconstr : 3.001314 kl : 12.332169 \n",
      "\n",
      "===> epoch: 95\n",
      "Train\telbo : 4.792008 reconstr : 3.57788 kl : 12.780296 \n",
      "Val\telbo : 4.173747 reconstr : 3.008788 kl : 12.262725 \n",
      "\n",
      "===> epoch: 96\n",
      "Train\telbo : 4.79979 reconstr : 3.580359 kl : 12.702414 \n",
      "Val\telbo : 4.176437 reconstr : 3.000514 kl : 12.249207 \n",
      "\n",
      "===> epoch: 97\n",
      "Train\telbo : 4.817997 reconstr : 3.587374 kl : 12.686828 \n",
      "Val\telbo : 4.191049 reconstr : 3.007811 kl : 12.198327 \n",
      "\n",
      "===> epoch: 98\n",
      "Train\telbo : 4.810195 reconstr : 3.574354 kl : 12.610621 \n",
      "Val\telbo : 4.205654 reconstr : 3.016471 kl : 12.13452 \n",
      "\n",
      "===> epoch: 99\n",
      "Train\telbo : 4.828007 reconstr : 3.576678 kl : 12.639689 \n",
      "Val\telbo : 4.227726 reconstr : 3.032966 kl : 12.068283 \n",
      "Summary of training:\n",
      "Let's plot some reconstructed data: \n",
      "reconstr_errors avg : 3.028233051300049, thresh : 4.226241111755371\n",
      " avg recontruction error \n",
      " \t normal data : 2.8722245693206787, anomalies : 7.656839847564697\n",
      "Quality measures of our classifier:\n",
      "\t precision : 0.8715447187423706\n",
      "\t recall : 0.8575999736785889\n",
      "\t F1_score : 0.8645160794258118\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "np.random.seed(66)\n",
    "torch.manual_seed(66)\n",
    "\n",
    "train_loader, val_loader, test_loader = get_data()\n",
    "model = VAE()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)\n",
    "\n",
    "trainer = VAE_Trainer(model = model,\n",
    "                      train_loader = train_loader, \n",
    "                      val_loader = val_loader, \n",
    "                      test_loader = test_loader, \n",
    "                      optimizer = optimizer)\n",
    "\n",
    "model = trainer.train(plot=True)\n",
    "plot_reconstr(model, test_loader)\n",
    "trainer.test()\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05cd002",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "8e0e7c798d85b07fb8909c9cf0db3c0d",
     "grade": false,
     "grade_id": "cell-2dc0541786fbbb9f",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "A vantage of having a probabilistic representation of the encodings is that we can extract uncertainties over the reconstructed data. In the following cell we will perform a simple Monte Carlo evaluation of the uncertainties and plot confidence intervals around the mean reconstruction of the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b7ecaf8",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "96508a3a693b0d22c594938349590c6c",
     "grade": false,
     "grade_id": "cell-ef2fbb66719b82f2",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "torch.manual_seed(1)\n",
    "data = next(iter(train_loader))['data'][0]\n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True)\n",
    "ax[0].plot(np.arange(350), data)\n",
    "\n",
    "model.eval()\n",
    "mean, logstd = model.encode(data)\n",
    "reconstructed_l = []\n",
    "for i in range(10):\n",
    "    z = model.reparameterize(mean, logstd)\n",
    "    reconstructed_l.append(model.decode(z).unsqueeze(0))\n",
    "reconstructed = torch.cat(reconstructed_l, axis=0)\n",
    "mean = reconstructed.mean(axis=0).detach().numpy()\n",
    "std = reconstructed.std(axis=0).detach().numpy()\n",
    "ax[1].plot(np.arange(350), mean)\n",
    "ax[1].fill_between(np.arange(350), mean + 2*std, mean - 2*std, alpha=0.2, edgecolor='blue')\n",
    "fig.show()\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d523f3",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "70accb14463699ad1b18e4c40c6cff36",
     "grade": false,
     "grade_id": "cell-31b96ff5858f1063",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "VAEs can be used to generate new datapoints. Once the network has been trained, we can sample latent representations from the prior distribution (in our case a standard gaussian) and decode them to produce new unseen data. The same mechanism is at the base of GANs. \n",
    "\n",
    "Try to produce new data yourself (the box is not graded)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83b1f5ba",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0188c6c0874eca4981aa197515f47358",
     "grade": false,
     "grade_id": "cell-52da14748ffd327a",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.manual_seed(1)\n",
    "size = model.embedding_dim\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)\n",
    "model.eval()\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        sample = torch.normal(0,1,(size,))           # sample from the prior (standard normal)\n",
    "        new_data = model.decode(sample).detach().numpy()       # feed the sample to the decoder to produce new data\n",
    "        # if you get an error in the plot function, just have a look at how plots were produced in the block above\n",
    "        axs[i,j].plot(np.arange(350), new_data)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106cd2a2",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "f44c0bbc8223408cf1d74a10f61ad009",
     "grade": false,
     "grade_id": "cell-c641a99949382c50",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "We have seen the basic structure of a VAE, tested it on a anomaly detection task and seen how new data can be generated by means of the decoder. We will now see how we can enforce simple properties on the representations in the latent space.\n",
    "\n",
    "By minimizing the KL we are forcing the representations (remember that these are distributions, not single values) produced by the encoder to be as close as possible to a standard normal. We could go beyond this and impose, for example, that the distributions of representations corresponding to different classes of data occupy distinct sub-volumes of the latent space.\n",
    "\n",
    "Our training set will now consist of ECGs of both 'normal' and 'PVC' classes. We will train, in parallel to a standard VAE (as above), a linear classifier that takes in input the means of the distributions produced by the encoder and outputs if the datapoint is normal or an anomaly. In this way the means of the distributions corresponding to the two different classes will be pushed in linearly separable volumes of the latent space."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17950592",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "d279f61f0e03fd865de48091bb857bc0",
     "grade": false,
     "grade_id": "cell-f4c9b1cc4a2be9d0",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Implement a simple classifier of one single fully connected layer. Select the right activation function for a binary classifier (you have already encountered it in a previous exercise).\n",
    "\n",
    "Hint: torch.nn module has useful functions..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "096b4ada",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "210a0558e1762eea8519a5c47938f2b9",
     "grade": false,
     "grade_id": "Composed_NN",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "class BinaryClassifier(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super().__init__()\n",
    "        self.fc = None           # f(ully)c(onnected)\n",
    "        self.activation = None   # we need a differentiable function with codomain (0,1)... \n",
    "\n",
    "        self.fc = nn.Linear(input_dim, 1)           # f(ully)c(onnected)\n",
    "        self.activation = nn.Sigmoid()   # we need a differentiable function with codomain (0,1)... \n",
    "        \n",
    "    def forward(self, data):\n",
    "        return self.activation(self.fc(data))\n",
    "    \n",
    "    \n",
    "class Composed(nn.Module):\n",
    "    def __init__(self, input_dim : int = 350, embedding_dim : int = 3):\n",
    "        super().__init__()\n",
    "        self.embedding_dim = embedding_dim\n",
    "        self.vae = VAE(input_dim, embedding_dim)\n",
    "        self.classifier = BinaryClassifier(embedding_dim)\n",
    "    def forward(self, data):\n",
    "        out, mean, logstd, z = self.vae(data)\n",
    "        logits = self.classifier(mean).squeeze()\n",
    "        return [out, mean, logstd, z, logits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2582a86d",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "857f2078c78d21715f9d002181b53799",
     "grade": true,
     "grade_id": "Binary_classifier",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "# ID: Binary_classifier - possible points: 2\n",
    "\n",
    "# hidden tests checking if the choice of layers is correct\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5459bc3",
   "metadata": {},
   "source": [
    "Try to implement the loss of our new architecture. It will have to be composed by 3 additive terms: the likelihood for the reconstruction, the (scaled!) kl divergence for the prior and a term for the binary classifier. \n",
    "\n",
    "(For this task you may drop the KL warmup factor)\n",
    "\n",
    "Hints: \n",
    "1. take a look at the VAE_Trainer loss function implemented above\n",
    "2. check ComposedTrainer's new attribute: class_loss \n",
    "3. remember that we already implemented a function for the KL divergence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f30588da",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "6e11a69f4531a3796ba30e993854c975",
     "grade": false,
     "grade_id": "cell-c199db04cd7f601a",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "class ComposedVAE_Trainer(AbstractTrainer):\n",
    "    def __init__(self, model : nn.Module, \n",
    "                 train_loader : Optional[DataLoader] = None, \n",
    "                 val_loader : Optional[DataLoader] = None,\n",
    "                 test_loader : Optional[DataLoader] = None, \n",
    "                 optimizer = None):\n",
    "        super().__init__(model=model,\n",
    "                         train_loader = train_loader,\n",
    "                         val_loader = val_loader,\n",
    "                         test_loader = test_loader,\n",
    "                         optimizer = optimizer,\n",
    "                         early_stop = 3)\n",
    "        self.class_loss = nn.BCELoss(reduction='sum')     ## for the reconstr_loss we set reduction='none'\n",
    "                                                          ## because of how I used it in the test function. \n",
    "                                                          ## In general it is easier to use reductions directly as\n",
    "                                                          ## in this case. Check what this reduction='sum' does.\n",
    "        self.measures = {'loss' : [], 'reconstr' : [], 'kl' : [], 'classif' : []}\n",
    "\n",
    "    def _loss(self, target, label, *args):\n",
    "        out = args[0] \n",
    "        mean = args[1]\n",
    "        logstd = args[2]\n",
    "        logits = args[4]\n",
    "        batch_dim = target.shape[0]\n",
    "        #                               <============================ THE FOLLOWING 4 LINES ONLY\n",
    "        reconstr_error = None      # an old friend\n",
    "        kl = None                  # another old friend\n",
    "        classif_error = None       # a new one\n",
    "        loss = None\n",
    "\n",
    "        reconstr_error = self.reconstr_loss(out, target).sum()/batch_dim\n",
    "        kl = KL_normnorm(mean, logstd)/batch_dim\n",
    "        classif_error = self.class_loss(logits, label.float())/batch_dim\n",
    "        loss = reconstr_error+kl+classif_error\n",
    "        loss = reconstr_error + kl*0.1*(self.ep/self.epochs) + classif_error\n",
    "        \n",
    "        return [loss, reconstr_error, kl, classif_error]\n",
    "\n",
    "    def test(self):\n",
    "        self.model.eval()\n",
    "        means = []\n",
    "        labels = []\n",
    "        preds = []\n",
    "        with torch.no_grad():\n",
    "            for batch in self.val_loader:\n",
    "                data = batch['data'].to(self.device)\n",
    "                label = batch['labels'].to(self.device).squeeze()\n",
    "                out, mean, logstd, z, logits = self.model(data)\n",
    "                means.append(mean)\n",
    "                labels.append(label)\n",
    "                preds.append((logits>0.5).float())\n",
    "        labels = torch.cat(labels).numpy().astype('int')\n",
    "        preds = torch.cat(preds).numpy().astype('int')\n",
    "        \n",
    "        fig, ax = plt.subplots()\n",
    "        cm = confusion_matrix(labels, preds, normalize='true')\n",
    "        sb.heatmap(cm, square=True, xticklabels=False, yticklabels=False, \n",
    "                    annot=True, cbar=False, cmap=\"Reds\", fmt='.2f').get_figure()\n",
    "        ax.set_title('Classification: confusion matrix')\n",
    "        fig.show()\n",
    "\n",
    "\n",
    "        means = torch.cat(means).numpy()\n",
    "        colors={0: 'blue', 1:'red'}\n",
    "        fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "        ax.scatter(means[:,0], means[:,1], means[:,2], marker=\".\", s=20, c=[colors[i] for i in labels])\n",
    "        ax.set_title('Latent space')\n",
    "        ax.set_xlim(-4., 4.)\n",
    "        ax.set_ylim(-4., 4.)\n",
    "        ax.set_zlim(-4., 4.)\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "901123a2",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "cb11cb33bab5b5887cf375b7801ff9bd",
     "grade": true,
     "grade_id": "Composed_Loss",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "# ID: Composed_Loss - possible points: 1\n",
    "\n",
    "from math import isclose\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)\n",
    "t = ComposedVAE_Trainer(model=Composed(1, 1), optimizer=optimizer)\n",
    "t.ep = 3\n",
    "data = torch.tensor([[12.],[3.]])\n",
    "label = torch.tensor([[1.],[0.]])\n",
    "out = torch.tensor([[1.5], [0.5]])\n",
    "mean = torch.tensor([[0.], [-0.5]])\n",
    "logstd = torch.tensor([[1.5], [-1.]])\n",
    "logits = torch.tensor([[0.8], [0.2]])\n",
    "z = torch.tensor([[.5], [1.5]])\n",
    "res = t._loss(data, label, out, mean, logstd, z, logits)\n",
    "for x_hat, x in zip(res, [6.7887, 6.5, 4.3677, 0.2231]):\n",
    "    assert isclose(x_hat, x, abs_tol=1e-1), 'wrong'\n",
    "\n",
    "\n",
    "# similar hidden test\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ce8f11",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "b758908f702e8f536c8865c6c9280c30",
     "grade": false,
     "grade_id": "cell-c298835b1ae392b2",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "We will now train our network for a cycle of 100 epochs, using as early stopping criteria the classification error on the validation set, with a scaling constant of 0.5 for the KL divergence and using (or not) KL warmup. \n",
    "\n",
    "Graphs produced are:\n",
    "1. summary of training\n",
    "2. confusion matrix associated to the binary classifier\n",
    "4. interactive 3D graph in the latent space of the means of the representation distributions produced by the encoder on the validation set, where blue points are 'normal' and red points are 'PVC' ECGs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0f668c79",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "2c4b36a97f95950b89fbb69414c7f500",
     "grade": false,
     "grade_id": "cell-988e9a53a12e2658",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===> epoch: 0\n",
      "Train\tloss : 44.393456 reconstr : 43.662792 kl : 0.046888 classif : 0.730664 \n",
      "Val\tloss : 16.79672 reconstr : 16.082246 kl : 0.138227 classif : 0.714474 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 1\n",
      "Train\tloss : 17.942441 reconstr : 17.239578 kl : 0.884899 classif : 0.701977 \n",
      "Val\tloss : 12.197844 reconstr : 11.505158 kl : 1.810413 classif : 0.690875 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 2\n",
      "Train\tloss : 12.032476 reconstr : 11.328963 kl : 2.85089 classif : 0.697812 \n",
      "Val\tloss : 9.892566 reconstr : 9.207434 kl : 3.616253 classif : 0.6779 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 3\n",
      "Train\tloss : 10.224733 reconstr : 9.560058 kl : 4.146543 classif : 0.652236 \n",
      "Val\tloss : 9.011332 reconstr : 8.386567 kl : 4.680751 classif : 0.610723 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 4\n",
      "Train\tloss : 9.315854 reconstr : 8.706501 kl : 5.016081 classif : 0.589288 \n",
      "Val\tloss : 7.964349 reconstr : 7.38282 kl : 5.533915 classif : 0.559394 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 5\n",
      "Train\tloss : 8.539792 reconstr : 7.964228 kl : 6.055967 classif : 0.545283 \n",
      "Val\tloss : 7.51154 reconstr : 6.964914 kl : 6.510943 classif : 0.514072 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 6\n",
      "Train\tloss : 8.049003 reconstr : 7.496811 kl : 6.852786 classif : 0.511075 \n",
      "Val\tloss : 7.119925 reconstr : 6.583296 kl : 7.148313 classif : 0.493739 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 7\n",
      "Train\tloss : 7.747228 reconstr : 7.208031 kl : 7.572827 classif : 0.486188 \n",
      "Val\tloss : 6.811319 reconstr : 6.285545 kl : 8.012079 classif : 0.46969 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 8\n",
      "Train\tloss : 7.531848 reconstr : 6.998312 kl : 8.265528 classif : 0.467411 \n",
      "Val\tloss : 6.514518 reconstr : 6.002013 kl : 8.656702 classif : 0.443251 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 9\n",
      "Train\tloss : 7.356634 reconstr : 6.83031 kl : 8.773525 classif : 0.447362 \n",
      "Val\tloss : 6.528984 reconstr : 6.02372 kl : 8.966198 classif : 0.424569 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 10\n",
      "Train\tloss : 7.136681 reconstr : 6.614969 kl : 9.247039 classif : 0.429241 \n",
      "Val\tloss : 6.24107 reconstr : 5.736296 kl : 9.547382 classif : 0.4093 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 11\n",
      "Train\tloss : 7.059631 reconstr : 6.538468 kl : 9.570901 classif : 0.415883 \n",
      "Val\tloss : 6.245481 reconstr : 5.741726 kl : 9.735397 classif : 0.396665 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 12\n",
      "Train\tloss : 7.01662 reconstr : 6.500032 kl : 9.801946 classif : 0.398965 \n",
      "Val\tloss : 6.167979 reconstr : 5.671858 kl : 9.91089 classif : 0.377191 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 13\n",
      "Train\tloss : 6.973697 reconstr : 6.45797 kl : 9.951879 classif : 0.386352 \n",
      "Val\tloss : 6.198584 reconstr : 5.700054 kl : 10.015081 classif : 0.368334 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 14\n",
      "Train\tloss : 6.953274 reconstr : 6.434774 kl : 10.093954 classif : 0.377185 \n",
      "Val\tloss : 6.100931 reconstr : 5.601585 kl : 10.231771 classif : 0.3561 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 15\n",
      "Train\tloss : 6.898747 reconstr : 6.378181 kl : 10.220405 classif : 0.36726 \n",
      "Val\tloss : 6.074413 reconstr : 5.566025 kl : 10.37315 classif : 0.35279 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 16\n",
      "Train\tloss : 6.866557 reconstr : 6.343171 kl : 10.297245 classif : 0.358631 \n",
      "Val\tloss : 6.183432 reconstr : 5.683571 kl : 10.214933 classif : 0.336422 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 17\n",
      "Train\tloss : 6.842281 reconstr : 6.318374 kl : 10.402694 classif : 0.347061 \n",
      "Val\tloss : 6.049556 reconstr : 5.5419 kl : 10.414224 classif : 0.330614 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 18\n",
      "Train\tloss : 6.801291 reconstr : 6.278918 kl : 10.414381 classif : 0.334915 \n",
      "Val\tloss : 6.083649 reconstr : 5.579052 kl : 10.43027 classif : 0.316851 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 19\n",
      "Train\tloss : 6.832063 reconstr : 6.306356 kl : 10.409423 classif : 0.327928 \n",
      "Val\tloss : 6.06038 reconstr : 5.556583 kl : 10.351002 classif : 0.307128 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 20\n",
      "Train\tloss : 6.746999 reconstr : 6.220916 kl : 10.405346 classif : 0.317976 \n",
      "Val\tloss : 5.955493 reconstr : 5.45172 kl : 10.406972 classif : 0.295634 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 21\n",
      "Train\tloss : 6.715763 reconstr : 6.190144 kl : 10.407362 classif : 0.307065 \n",
      "Val\tloss : 6.006184 reconstr : 5.498649 kl : 10.419155 classif : 0.288732 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 22\n",
      "Train\tloss : 6.708838 reconstr : 6.180583 kl : 10.430345 classif : 0.298788 \n",
      "Val\tloss : 5.936327 reconstr : 5.425823 kl : 10.435146 classif : 0.280931 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 23\n",
      "Train\tloss : 6.698279 reconstr : 6.167828 kl : 10.40151 classif : 0.291216 \n",
      "Val\tloss : 5.945087 reconstr : 5.437105 kl : 10.422854 classif : 0.268257 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 24\n",
      "Train\tloss : 6.70622 reconstr : 6.176746 kl : 10.313845 classif : 0.281942 \n",
      "Val\tloss : 5.937846 reconstr : 5.428594 kl : 10.298306 classif : 0.262093 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 25\n",
      "Train\tloss : 6.683286 reconstr : 6.149165 kl : 10.293222 classif : 0.276791 \n",
      "Val\tloss : 5.946732 reconstr : 5.429461 kl : 10.23559 classif : 0.261382 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 26\n",
      "Train\tloss : 6.67837 reconstr : 6.142533 kl : 10.244636 classif : 0.269476 \n",
      "Val\tloss : 5.929787 reconstr : 5.415692 kl : 10.218807 classif : 0.248406 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 27\n",
      "Train\tloss : 6.684477 reconstr : 6.148202 kl : 10.230916 classif : 0.26004 \n",
      "Val\tloss : 6.032999 reconstr : 5.516297 kl : 10.152068 classif : 0.242595 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 28\n",
      "Train\tloss : 6.675553 reconstr : 6.136493 kl : 10.178727 classif : 0.254057 \n",
      "Val\tloss : 5.866488 reconstr : 5.345252 kl : 10.136742 classif : 0.237407 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 29\n",
      "Train\tloss : 6.670922 reconstr : 6.128994 kl : 10.129245 classif : 0.24818 \n",
      "Val\tloss : 5.89249 reconstr : 5.366968 kl : 10.119919 classif : 0.232045 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 30\n",
      "Train\tloss : 6.649757 reconstr : 6.102097 kl : 10.107519 classif : 0.244434 \n",
      "Val\tloss : 5.876228 reconstr : 5.347889 kl : 10.149164 classif : 0.223864 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 31\n",
      "Train\tloss : 6.634576 reconstr : 6.085277 kl : 10.118097 classif : 0.235638 \n",
      "Val\tloss : 5.952247 reconstr : 5.419999 kl : 10.140093 classif : 0.217906 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 32\n",
      "Train\tloss : 6.647033 reconstr : 6.094156 kl : 10.076712 classif : 0.230423 \n",
      "Val\tloss : 5.921548 reconstr : 5.384602 kl : 10.028621 classif : 0.21603 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 33\n",
      "Train\tloss : 6.635872 reconstr : 6.078397 kl : 10.042036 classif : 0.226088 \n",
      "Val\tloss : 5.877511 reconstr : 5.337029 kl : 10.042649 classif : 0.209075 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 34\n",
      "Train\tloss : 6.64463 reconstr : 6.084812 kl : 10.012926 classif : 0.219378 \n",
      "Val\tloss : 5.913504 reconstr : 5.370096 kl : 10.012876 classif : 0.202971 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 35\n",
      "Train\tloss : 6.638268 reconstr : 6.072476 kl : 9.972613 classif : 0.21675 \n",
      "Val\tloss : 5.947404 reconstr : 5.397403 kl : 9.98393 classif : 0.200563 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 36\n",
      "Train\tloss : 6.651742 reconstr : 6.081598 kl : 9.951036 classif : 0.211906 \n",
      "Val\tloss : 5.944193 reconstr : 5.389371 kl : 9.877831 classif : 0.19922 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 37\n",
      "Train\tloss : 6.659309 reconstr : 6.082603 kl : 9.889962 classif : 0.210777 \n",
      "Val\tloss : 5.944574 reconstr : 5.384679 kl : 9.8282 classif : 0.196251 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 38\n",
      "Train\tloss : 6.657784 reconstr : 6.077554 kl : 9.822271 classif : 0.206984 \n",
      "Val\tloss : 5.921208 reconstr : 5.357294 kl : 9.851173 classif : 0.18957 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 39\n",
      "Train\tloss : 6.676712 reconstr : 6.090784 kl : 9.78496 classif : 0.204315 \n",
      "Val\tloss : 5.931907 reconstr : 5.363471 kl : 9.76944 classif : 0.187428 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 40\n",
      "Train\tloss : 6.639971 reconstr : 6.049102 kl : 9.767039 classif : 0.200187 \n",
      "Val\tloss : 5.898341 reconstr : 5.323794 kl : 9.804742 classif : 0.182357 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 41\n",
      "Train\tloss : 6.642367 reconstr : 6.043056 kl : 9.759492 classif : 0.199172 \n",
      "Val\tloss : 5.925253 reconstr : 5.344612 kl : 9.71577 classif : 0.182294 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 42\n",
      "Train\tloss : 6.650891 reconstr : 6.048832 kl : 9.741916 classif : 0.192898 \n",
      "Val\tloss : 5.964653 reconstr : 5.377329 kl : 9.715073 classif : 0.17929 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 43\n",
      "Train\tloss : 6.653516 reconstr : 6.041955 kl : 9.707368 classif : 0.194144 \n",
      "Val\tloss : 5.943212 reconstr : 5.351449 kl : 9.630676 classif : 0.177643 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 44\n",
      "Train\tloss : 6.655929 reconstr : 6.039645 kl : 9.662732 classif : 0.191123 \n",
      "Val\tloss : 5.951427 reconstr : 5.35033 kl : 9.663014 classif : 0.175924 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 45\n",
      "Train\tloss : 6.668786 reconstr : 6.047203 kl : 9.617269 classif : 0.188806 \n",
      "Val\tloss : 5.974804 reconstr : 5.371147 kl : 9.572223 classif : 0.172907 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 46\n",
      "Train\tloss : 6.673259 reconstr : 6.039702 kl : 9.570485 classif : 0.193315 \n",
      "Val\tloss : 6.019176 reconstr : 5.409732 kl : 9.517501 classif : 0.171639 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 47\n",
      "Train\tloss : 6.677225 reconstr : 6.045 kl : 9.524722 classif : 0.184563 \n",
      "Val\tloss : 6.029395 reconstr : 5.413337 kl : 9.469478 classif : 0.170992 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 48\n",
      "Train\tloss : 6.68288 reconstr : 6.045872 kl : 9.490195 classif : 0.181479 \n",
      "Val\tloss : 5.946085 reconstr : 5.324632 kl : 9.470026 classif : 0.166892 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 49\n",
      "Train\tloss : 6.666418 reconstr : 6.022282 kl : 9.466719 classif : 0.180266 \n",
      "Val\tloss : 5.966083 reconstr : 5.335995 kl : 9.452473 classif : 0.166917 \n",
      "\n",
      "===> epoch: 50\n",
      "Train\tloss : 6.679719 reconstr : 6.029068 kl : 9.435069 classif : 0.178898 \n",
      "Val\tloss : 5.970582 reconstr : 5.336072 kl : 9.410944 classif : 0.163963 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 51\n",
      "Train\tloss : 6.685697 reconstr : 6.02832 kl : 9.433676 classif : 0.176259 \n",
      "Val\tloss : 6.031797 reconstr : 5.392818 kl : 9.377569 classif : 0.160723 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 52\n",
      "Train\tloss : 6.697093 reconstr : 6.032997 kl : 9.40199 classif : 0.175193 \n",
      "Val\tloss : 5.981229 reconstr : 5.330187 kl : 9.39988 classif : 0.162248 \n",
      "\n",
      "===> epoch: 53\n",
      "Train\tloss : 6.702396 reconstr : 6.024801 kl : 9.378632 classif : 0.180527 \n",
      "Val\tloss : 6.006596 reconstr : 5.347698 kl : 9.390331 classif : 0.16121 \n",
      "\n",
      "===> epoch: 54\n",
      "Train\tloss : 6.693811 reconstr : 6.017982 kl : 9.326439 classif : 0.172201 \n",
      "Val\tloss : 6.001855 reconstr : 5.336542 kl : 9.307726 classif : 0.162695 \n",
      "\n",
      "===> epoch: 55\n",
      "Train\tloss : 6.705243 reconstr : 6.021979 kl : 9.29464 classif : 0.172058 \n",
      "Val\tloss : 6.046316 reconstr : 5.381222 kl : 9.184226 classif : 0.159962 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 56\n",
      "Train\tloss : 6.714666 reconstr : 6.029603 kl : 9.188537 classif : 0.170504 \n",
      "Val\tloss : 6.010406 reconstr : 5.336991 kl : 9.192388 classif : 0.158641 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 57\n",
      "Train\tloss : 6.718374 reconstr : 6.024327 kl : 9.197249 classif : 0.169803 \n",
      "Val\tloss : 6.024868 reconstr : 5.347915 kl : 9.133259 classif : 0.156357 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 58\n",
      "Train\tloss : 6.712375 reconstr : 6.01082 kl : 9.191562 classif : 0.168444 \n",
      "Val\tloss : 6.008022 reconstr : 5.318769 kl : 9.173191 classif : 0.157208 \n",
      "\n",
      "===> epoch: 59\n",
      "Train\tloss : 6.727503 reconstr : 6.022183 kl : 9.130277 classif : 0.166633 \n",
      "Val\tloss : 6.045858 reconstr : 5.354413 kl : 9.086557 classif : 0.155338 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 60\n",
      "Train\tloss : 6.72034 reconstr : 6.010578 kl : 9.082286 classif : 0.164825 \n",
      "Val\tloss : 5.998516 reconstr : 5.301527 kl : 9.062089 classif : 0.153264 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 61\n",
      "Train\tloss : 6.739101 reconstr : 6.020529 kl : 9.066412 classif : 0.165521 \n",
      "Val\tloss : 6.032567 reconstr : 5.32616 kl : 9.068997 classif : 0.153198 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 62\n",
      "Train\tloss : 6.738888 reconstr : 6.013169 kl : 9.052173 classif : 0.164484 \n",
      "Val\tloss : 6.024163 reconstr : 5.312677 kl : 9.012784 classif : 0.152693 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 63\n",
      "Train\tloss : 6.732118 reconstr : 5.998004 kl : 9.013623 classif : 0.166256 \n",
      "Val\tloss : 6.093873 reconstr : 5.378206 kl : 8.963045 classif : 0.150995 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 64\n",
      "Train\tloss : 6.750251 reconstr : 6.013683 kl : 8.957705 classif : 0.163276 \n",
      "Val\tloss : 6.078409 reconstr : 5.357332 kl : 8.910133 classif : 0.150829 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 65\n",
      "Train\tloss : 6.758092 reconstr : 6.010351 kl : 8.926785 classif : 0.1675 \n",
      "Val\tloss : 6.078421 reconstr : 5.350848 kl : 8.899825 classif : 0.149084 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 66\n",
      "Train\tloss : 6.756884 reconstr : 5.99995 kl : 8.902782 classif : 0.16935 \n",
      "Val\tloss : 6.079192 reconstr : 5.343392 kl : 8.886841 classif : 0.149269 \n",
      "\n",
      "===> epoch: 67\n",
      "Train\tloss : 6.761992 reconstr : 6.007768 kl : 8.873284 classif : 0.159714 \n",
      "Val\tloss : 6.104054 reconstr : 5.365354 kl : 8.808186 classif : 0.148551 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 68\n",
      "Train\tloss : 6.780553 reconstr : 6.020687 kl : 8.819339 classif : 0.160151 \n",
      "Val\tloss : 6.107633 reconstr : 5.360918 kl : 8.797222 classif : 0.148504 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 69\n",
      "Train\tloss : 6.782561 reconstr : 6.018886 kl : 8.775297 classif : 0.158179 \n",
      "Val\tloss : 6.097795 reconstr : 5.3441 kl : 8.784091 classif : 0.147593 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 70\n",
      "Train\tloss : 6.775058 reconstr : 6.0046 kl : 8.740503 classif : 0.158623 \n",
      "Val\tloss : 6.082383 reconstr : 5.323759 kl : 8.747995 classif : 0.146265 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 71\n",
      "Train\tloss : 6.778563 reconstr : 5.999626 kl : 8.727756 classif : 0.159267 \n",
      "Val\tloss : 6.120048 reconstr : 5.355633 kl : 8.711691 classif : 0.145885 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 72\n",
      "Train\tloss : 6.786136 reconstr : 6.001735 kl : 8.707504 classif : 0.157461 \n",
      "Val\tloss : 6.096795 reconstr : 5.330678 kl : 8.639287 classif : 0.144088 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 73\n",
      "Train\tloss : 6.787395 reconstr : 5.998298 kl : 8.661915 classif : 0.156777 \n",
      "Val\tloss : 6.108509 reconstr : 5.332783 kl : 8.632267 classif : 0.145571 \n",
      "\n",
      "===> epoch: 74\n",
      "Train\tloss : 6.80498 reconstr : 6.008952 kl : 8.646534 classif : 0.156184 \n",
      "Val\tloss : 6.122719 reconstr : 5.341304 kl : 8.605964 classif : 0.144574 \n",
      "\n",
      "===> epoch: 75\n",
      "Train\tloss : 6.819471 reconstr : 6.017851 kl : 8.603429 classif : 0.156362 \n",
      "Val\tloss : 6.124759 reconstr : 5.334832 kl : 8.61716 classif : 0.14364 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 76\n",
      "Train\tloss : 6.803509 reconstr : 5.994457 kl : 8.603769 classif : 0.155165 \n",
      "Val\tloss : 6.117113 reconstr : 5.320424 kl : 8.593668 classif : 0.14357 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 77\n",
      "Train\tloss : 6.832492 reconstr : 6.019613 kl : 8.564947 classif : 0.153378 \n",
      "Val\tloss : 6.173447 reconstr : 5.370807 kl : 8.558699 classif : 0.14362 \n",
      "\n",
      "===> epoch: 78\n",
      "Train\tloss : 6.827094 reconstr : 6.007371 kl : 8.526031 classif : 0.154692 \n",
      "Val\tloss : 6.167955 reconstr : 5.358136 kl : 8.543848 classif : 0.143399 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 79\n",
      "Train\tloss : 6.843513 reconstr : 6.016216 kl : 8.516795 classif : 0.15447 \n",
      "Val\tloss : 6.168964 reconstr : 5.353135 kl : 8.513764 classif : 0.143241 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 80\n",
      "Train\tloss : 6.839702 reconstr : 6.006529 kl : 8.500935 classif : 0.153098 \n",
      "Val\tloss : 6.122707 reconstr : 5.298022 kl : 8.516973 classif : 0.143327 \n",
      "\n",
      "===> epoch: 81\n",
      "Train\tloss : 6.835408 reconstr : 5.994188 kl : 8.469818 classif : 0.155165 \n",
      "Val\tloss : 6.181637 reconstr : 5.357457 kl : 8.419727 classif : 0.142182 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 82\n",
      "Train\tloss : 6.859004 reconstr : 6.013332 kl : 8.422661 classif : 0.155014 \n",
      "Val\tloss : 6.162839 reconstr : 5.330329 kl : 8.421754 classif : 0.141926 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 83\n",
      "Train\tloss : 6.857235 reconstr : 6.005186 kl : 8.409011 classif : 0.154101 \n",
      "Val\tloss : 6.188487 reconstr : 5.353725 kl : 8.355746 classif : 0.141236 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 84\n",
      "Train\tloss : 6.861693 reconstr : 6.004369 kl : 8.391944 classif : 0.1524 \n",
      "Val\tloss : 6.163799 reconstr : 5.319819 kl : 8.350922 classif : 0.142502 \n",
      "\n",
      "===> epoch: 85\n",
      "Train\tloss : 6.878935 reconstr : 6.016669 kl : 8.351584 classif : 0.152381 \n",
      "Val\tloss : 6.156627 reconstr : 5.305633 kl : 8.35669 classif : 0.140675 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 86\n",
      "Train\tloss : 6.884865 reconstr : 6.016277 kl : 8.332828 classif : 0.151965 \n",
      "Val\tloss : 6.205738 reconstr : 5.350153 kl : 8.301534 classif : 0.141653 \n",
      "\n",
      "===> epoch: 87\n",
      "Train\tloss : 6.864652 reconstr : 5.990219 kl : 8.296718 classif : 0.152619 \n",
      "Val\tloss : 6.199049 reconstr : 5.340997 kl : 8.255037 classif : 0.139864 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 88\n",
      "Train\tloss : 6.897451 reconstr : 6.021091 kl : 8.257976 classif : 0.149658 \n",
      "Val\tloss : 6.205298 reconstr : 5.341519 kl : 8.234676 classif : 0.139127 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 89\n",
      "Train\tloss : 6.89097 reconstr : 6.009082 kl : 8.21745 classif : 0.150535 \n",
      "Val\tloss : 6.230233 reconstr : 5.359414 kl : 8.219978 classif : 0.139241 \n",
      "\n",
      "===> epoch: 90\n",
      "Train\tloss : 6.871375 reconstr : 5.981499 kl : 8.214454 classif : 0.150575 \n",
      "Val\tloss : 6.220365 reconstr : 5.346875 kl : 8.160165 classif : 0.139075 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 91\n",
      "Train\tloss : 6.891108 reconstr : 5.998445 kl : 8.170874 classif : 0.149113 \n",
      "Val\tloss : 6.233311 reconstr : 5.353211 kl : 8.145127 classif : 0.138893 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 92\n",
      "Train\tloss : 6.902341 reconstr : 6.004214 kl : 8.144181 classif : 0.148863 \n",
      "Val\tloss : 6.247072 reconstr : 5.363537 kl : 8.095879 classif : 0.138714 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 93\n",
      "Train\tloss : 6.918206 reconstr : 6.014084 kl : 8.131294 classif : 0.147911 \n",
      "Val\tloss : 6.245541 reconstr : 5.352018 kl : 8.120193 classif : 0.138345 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 94\n",
      "Train\tloss : 6.920948 reconstr : 6.009261 kl : 8.117816 classif : 0.148612 \n",
      "Val\tloss : 6.233867 reconstr : 5.334418 kl : 8.104409 classif : 0.137635 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 95\n",
      "Train\tloss : 6.92678 reconstr : 6.009097 kl : 8.099056 classif : 0.148272 \n",
      "Val\tloss : 6.227105 reconstr : 5.320733 kl : 8.093218 classif : 0.137516 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 96\n",
      "Train\tloss : 6.924644 reconstr : 6.003269 kl : 8.066511 classif : 0.14699 \n",
      "Val\tloss : 6.266421 reconstr : 5.355601 kl : 8.055445 classif : 0.137497 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 97\n",
      "Train\tloss : 6.939092 reconstr : 6.011016 kl : 8.038728 classif : 0.148319 \n",
      "Val\tloss : 6.254074 reconstr : 5.339295 kl : 8.013659 classif : 0.137454 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 98\n",
      "Train\tloss : 6.93841 reconstr : 6.006147 kl : 8.000555 classif : 0.148208 \n",
      "Val\tloss : 6.29892 reconstr : 5.38116 kl : 7.971577 classif : 0.136546 \n",
      "Model saved\n",
      "\n",
      "===> epoch: 99\n",
      "Train\tloss : 6.946341 reconstr : 6.01002 kl : 7.959347 classif : 0.148346 \n",
      "Val\tloss : 6.291657 reconstr : 5.368516 kl : 7.934952 classif : 0.137581 \n",
      "\n",
      " Best classif on validation: 0.136546\n",
      "Summary of training:\n",
      "Let's plot some reconstructed data: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Sra2t4tvf/rZISEgQANRl08pS6X/+859Oz6u8rqvlm0RaUJYnu1p2/NZbb4nzzjtPxMTEiJiYGDFhwgRx5513iiNHjjgdt3nzZnHxxReLuLg4ERMTI6ZNmyaeffZZp2M+/fRTce6554qoqChhNpvFFVdcIQ4ePOjWWPqeQ9atWyeuvPJKkZWVJQwGg8jKyhI33HCD+Prrr50e9+6774pJkyaprSGUz92CBQvE5MmTXf4+bDab+NnPfiZSUlJEdHS0WLRokTh27Fi/pdJCCFFXVydWrFghsrOzhcFgEDk5OeLmm28WtbW1Q45BCCF27dolrr76apGcnCyMRqPIz88X3/rWt8S6deucXuezzz4TRUVFwmAwiFGjRok1a9YM2JoinElCsJow1C1duhQHDhxwOZdKREQUbFjzEmI6Ojqcfj569Cg+/PBDXHDBBdoMiIiIyMuYeQkxmZmZWLZsGUaNGoXTp0/j+eefR1dXF3bt2uWyfwAREVGwYcFuiFm8eDH+/ve/o7KyEkajEfPmzcOvfvUrBi5ERBQymHkhIiKioMKaFyIiIgoqDF6IiIgoqIRczYvdbkd5eTni4uLYzp1II0IItLS0ICsrCzpdcFwj8dxBpC1PzhshF7yUl5er++8QkbZKS0uRk5Oj9TDcwnMHUWBw57wRcsFLXFwcAPnNK/vwEJF/NTc3Izc3V/08BgOeO4i05cl5I+SCFyXdazabeQIi0lgwTb/w3EEUGNw5bwTHZDQRERGRA4MXIiIiCioMXoiIiCioMHghIiKioMLghYiIiIIKgxciIiIKKgxeiIiIKKgweCEiIqKgwuCFiIiIggqDFyIiIgoqDF6IiIgoqDB4ISIioqDC4IXCVofFhoqmDgDA9lP1eGvHGY1HRERE7gi5XaWJ3FFa346r/vAF6tq6cP/iCVj90WEAwMRMMyZlcUdhIqJAxswLhaXXvypBbWsXhIAauADA6bo2DUdFRETuYPBCYWl3aaPL28saO/w7ECIi8hiDFwo7QgjsLW1yed+ZBgYvRESBjsELhZ3aVgtauqyQJOCHF42BJAHFE9MBMPNCRBQMWLBLYUcJUNLjTLi7eBxuObcQe8404tNDVThe06rx6IiIaCjMvFDYKXNMDWUnRkGvk5AYY8DU7HgAwImaNjR3dms5PCIiGgKDFwo7ZY3tAIDshCj1tuRYo/rz/jOu62GIiCgwMHihsKMU5Wb1Cl4AuccLAE4dEREFOAYvFHb2lcmZlfEZsU635yTKwUxZY6ffx0RERO5j8EJhpctqw4HyZgDAzNxEp/uyEkwAgHKuOCIiCmgMXiis7DzdCIvVjsToSOQnRzvdp0wjKfsdERFRYGLwQmHl/b3lAICLJ6VDkiSn+5TgpZzTRkREAY3BC4UNIQTWH64GACyZmtnvfmW1UWVzJ6w2u1/HRkRE7mPwQmHjZG0bKpo6YdDrMLcwud/9qbFGROol2OwC1S1dGoyQiIjcweCFwsb2Uw0AgJl5CYgy6Pvdr9NJyIhn0S4RUaDzS/Dy3HPPoaCgACaTCXPnzsW2bdsGPPb//b//h/nz5yMxMRGJiYkoLi4e9Hgidx2tbgHQ08/Flcx4Zbk0gxciokDl8+DljTfewD333IOHHnoIO3fuxPTp07Fo0SJUV1e7PH7jxo244YYbsGHDBmzZsgW5ubm45JJLUFZW5uuhUog7Wi03nxubHjvgMdks2iUiCng+D16eeuop3H777Vi+fDkmTZqENWvWIDo6Gi+99JLL41999VX84Ac/wIwZMzBhwgS8+OKLsNvtWLduna+HSiHuaJUcvIxJHTh4GZ0aAwDYcqLOL2MiIiLP+TR4sVgs2LFjB4qLi3teUKdDcXExtmzZ4tZztLe3o7u7G0lJSS7v7+rqQnNzs9MXUV+1rV3qVNCEjIGnjS6flgUA2Hy0Bg1tFr+MjbTBcwdR8PJp8FJbWwubzYb09HSn29PT01FZWenWc/zsZz9DVlaWUwDU2+rVqxEfH69+5ebmjnjcFHp2nJaLdcelxyI+OnLA4wpSYpCdEAW74B5HoY7nDqLgFdCrjR5//HG8/vrrePvtt2EymVwes3LlSjQ1NalfpaWlfh4lBYM9pY0AgKL8xMEPBJCXJHfePV3X7sshkcZ47iAKXhG+fPKUlBTo9XpUVVU53V5VVYWMjIxBH/vkk0/i8ccfx6effopp06YNeJzRaITRaPTKeCl0na6XA5HRg9S7KPKTo7HlRJ36GApNPHcQBS+fZl4MBgOKioqcim2V4tt58+YN+Ljf/OY3ePTRR7F27VrMnj3bl0OkMHHGEYjkJkUPcSSQ59jzqKSuzadjIiKi4fFp5gUA7rnnHtx8882YPXs25syZg6effhptbW1Yvnw5AOCmm25CdnY2Vq9eDQD49a9/jQcffBCvvfYaCgoK1NqY2NhYxMYOfdVM5Eppg1ysm5MYNeSx+UnyiqMSZl6IiAKSz4OX6667DjU1NXjwwQdRWVmJGTNmYO3atWoRb0lJCXS6ngTQ888/D4vFgm9+85tOz/PQQw9h1apVvh4uhaC2LivqHSuHchKHzrwou00zeCEiCkw+D14AYMWKFVixYoXL+zZu3Oj086lTp3w/IAorpxzTPwnRkYiPGnilkUKZNqpttaC1y4pYo18+JkRE5KaAXm1E5A3HlM66ae5NO5pNkUh0LKcu4YojIqKAw+CFQp4SvIxxM3gBgLxkue6ltIHBCxFRoGHwQiFP3RYgLc7tx2Sa5b5CVc3c44iIKNAweKGQp+wm7e60EQCkm+X+H5VNDF6IiAINgxcKaRarHaccdSueTBulx8uZl0pmXoiIAg6DFwppp+vaYLMLxBj0yIx3vcWEKxmcNiIiClgMXiik7SppBACMy4iDJEluP04JXjhtREQUeBi8UEj7+KDcofmCcWkePU6ZNqpq7vL6mIgosAkhIITQehg0CAYvFNJ2OjIvF05I9ehxSualtcuK1i6rt4dFRAFICIGH3t2PwpUfYs6v1mH7qXqth0QDYPBCIauz26ZuC6DsV+SuGGME4hyddTl1RBQejlW34s9bTgMAalq68M01W/D0p1+jqaNb45FRXwxeKGRVOIKOaIMe5ijPW/z3TB0xeCEKdW/vOoNv/P7zfrc//elRTH/4Y/xje6kGo6KBMHihkFXRKO8knRlv8qhYV8GiXaLwIITAj9/Yg45uGwDg1vMK8Zdb5jgd87cvT2sxNBoAgxcKWeWOoCMrIWpYj89wZF7KHEEQEYWepo5unKhtc7rtvDEpOH9cKk49fhm+e/4oAD2ZXAoM3C6XQtaJGnlbgOxhBi/j0+XtBA6WN3ttTEQUOCqbOrHwtxvR7si4AMCHP5qPSVlm9efvLhiNP246gZqWLnR222CK1GsxVOqDmRcKWdtOyisFZuUlDuvxk7PlE9j+8iavjYmIAsfWk3Vos9igrIq+ZlaOU+ACAInRkYgzydf5JfXcqDVQMPNCIamz24Y9ZxoBAHNHJQ3rOSZnxQMAzjR0oLHdgoRog7eGR0Qa21/WhLte3w0AWDghDeeOScFl0zL7HSdJEsakxWJXSSP2lDZiXLr7G7yS7zDzQiGppL4d3TaBOGME8pKih/Uc8VGR6mM5dUQUWv6y5ZT6/XljU3DLeYVIN7veQuT8sXKfqE8PVfljaOQGBi8Ukk47NmPMS44e1kojxRROHRGFpEMVLer3xRPTBz124US5Q/cXx+pgs7PzbiBg8EIh6XSdvHogP3l4WReFMnV0gJkXopBR29qlXpBsWXkRcofIzk7OikesMQItXVYcruS5IBAweKGQpBTW5XnYWbevMWmxAIATNW1DHElEweKjfRUQApiWE4/M+KFXI+p1Embly4X/O083+Hp45AYGLxSSlGCjMGVkmZfRqTGO52vlRm1EIaC1y4onP/4aAHC5iwLdgYxKkc8FZY3s9xIIGLxQSDpaLc9njx3hyoC8pBjoJKDNYkN1C3eYJgp2u0sa0dTRjbQ4I5adU+j249LMRgBAdQuDl0DA4IVCTlNHN6qa5UBDmfYZLkOETp0PP1nLqSOiYKfUupxVkARDhPt/AtPi5JVINbyICQgMXijkHKuWO+tmmE0wmyJH/HzKcukzDdwmgCiYtXR240+bTwLoaULprnQl89LM4CUQMHihkHNMnTIaWdZFkZMoF/SdaWB3TaJgtv5wtZo5WTQ5w6PHKpkXThsFBgYvFHK+rpIzLyOdMlLkJDLzQhQKSh2rEC+bmonRqZ6dH5TMS0N7N1q7rF4fG3mGwQuFnKOOaSNvtfFm5oUoNCgXIMO5sImPikRuknwu2HqizqvjIs8xeKGQYrXZsbtE7sMwMdOzOe2B9AQvzLwQBSshBD4/XgsAQzalc0WSJCwYJ28T8N+jtV4dG3mOwQuFlF2ljWjutCIhOhJTs+O98pzKtFFFUyesNrtXnpOI/Ou9PeUorZcvQHITh25M50qRo1HdwQp22dUagxcKKV8el9O5545JgV43/D2NekuNNcKg18FmF6hoYrEeUTDafqqnM+7UnOFd2IxNk6eij1a1sGmlxhi8UEjZXdoIAJiVl+i159TpJGRz6ogoqFU0yZ/dXy6dgmhDxLCeY0xaLHSSXLRb08ol01pi8EIhQwiBPWcaAQAzchO8+tws2iUKbuWOtv5ZCaZhP4cpUo/8ZHmbgKOOVY2kDQYvFDKaOrpR22oBAEzyUrGuIsOs9Hjg1RZRMKpsloMXdzZiHMw4R/+oI5UtIx4TDR+DFwoZyk7SaXFGRBn0Xn3u1Di5xwNbgxMFn85uG+rb5AubrBEHL466l2oGL1pi8EIhQwlehrMMcihq8MJ5bqKgo+xLZjZFwBw1vHoXhRK8MPOiLQYvFDKUZZB5vgxemHkhCjpfV8mBxrj0OEjSyFYhqpmXqlauONIQgxcKGT7NvMTKwUstgxeioKMU13pjv7PClBhE6CS0dFnZOkFDDF4oZCj7ljDzQkS9KfUpSp+WkTBE6FCYIq84UjI65H8MXihklPgweElxBC8tXVZ0WGxef34i8h0l8+Kt/c7GZcjPw+BFOwxeKCRYbXaUN/qu5iXOGAFjhPxxqWXRLlHQ6Oy24VSdXLA7zgvTRgAwLk0JXtjrRSsMXigklDd2wmoXMETokObIkniTJEnq1BF7vRAFj2PVrbALeVfoVC+dG8ZnyEHQUWZeNMPghULCjpJ6AMCEjDjovLSnUV+seyEKPu/sKgMgb6o40pVGirFqrxeuONIKgxcKCZuPyhsynjM6xWevoaw4Yq8XouDQ1mXFG9tLAQD/c3a+1543NzEakgS0W2yoczS/I/9i8EJBr7mzG2v3VwAAzh/nw+CFmReioPLBvgq0dFpRkByNBeNSvfa8hgidumWIssqR/IvBCwW9N7aVos1iw9i0WMwbleyz18mMl09WpxzdOokosG07KU8nXzYt0+vTybmJ8sIA7jSvDQYvFNSsNjte+eIUAOC2+YVem9N2ZVZ+IgBg68k6znMTBYFdJQ0AgFl5iV5/bmWn+VLuNK8JBi8U1D4+WIWyxg4kxxhw5Yxsn77WrLxEGPQ6VDV34Vg1l0gSBbIOiw3Ha+Qs6fTcBK8/f7YjeKloZJddLTB4oaC27lA1AOCbs3NgivTuTtJ9mSL1OGeMPC31wb4Kn74WEY1MeZM8nRNrjEByjMHrz5+ibBnCAn5NMHihoLbTkRY+24e1Lr0tmZoJAPj8WK1fXo+IhkdpWpmVYPLJdDKDF20xeKGgVdvapW51PyvX+3ParuQ7uvfWtnJ5JFEg6wleonzy/MmxcjanjucCTTB4oaClZD8mZMQhPjrSL6+ZzKstoqBQ5qhF8VXwksK+T5pi8EJBSwlezvdi/4ahpDiutlo6reiycoNGokD1daXcul9Z0uxtStPKlk4rOrt5LvA3Bi8UtPaeaQIAnFWQ5LfXNJsiEeHoF1HPzppEAanbZsdmx8XNOaN9Uw9njopApF4+FzAT638MXigodVlt6nLlSVlmv72uTichKYZz3USB7FBFM1q7rEiMjsTU7HifvIYkSeoO9kfZOsHvGLxQUDpW3QqrXcBsikCWo/Otvyh1L9zThCgwna6TG8eNTo312UatADA5Sw6MDpY3++w1yDUGLxSUthyXN2Kcnpvg0666riSrmRemiokCkdL1NjfJN/UuiinZctZ3f1mTT1+H+mPwQkHp00NVAIALx6f5/bW5RJIosJXWy8ukcxN9s9JIMTo1FgBQws0Z/c4vwctzzz2HgoICmEwmzJ07F9u2bRvw2AMHDuCaa65BQUEBJEnC008/7Y8hUhApa+zAVseGaxdPSvf76yfHOJZLtzHzQhSIzjgyLzk+zrxkxsvBUWUTtwjwN58HL2+88QbuuecePPTQQ9i5cyemT5+ORYsWobq62uXx7e3tGDVqFB5//HFkZGT4engUhD7cWwEhgLNHJfk8LewKMy9Ega3UkQnJ83nwItfb1bVZuFzaz3wevDz11FO4/fbbsXz5ckyaNAlr1qxBdHQ0XnrpJZfHn3XWWXjiiSdw/fXXw2g0+np4FIS2n5azLlpMGQE9vV5Y80IUeGx2gTJHd11fX9wkREfCFCn/Ga1qZvbFn3wavFgsFuzYsQPFxcU9L6jTobi4GFu2bPHKa3R1daG5udnpi0KXEAI7SxoBAEX5/tkSoC9l2oirjYIbzx2hqaq5E902gUi9hAyzb1ciSpKkTh1VcOrIr3wavNTW1sJmsyE93bkuIT09HZWVlV55jdWrVyM+Pl79ys3N9crzUmA609CBmpYuROolTPFR/4ahcNooNPDcEZqUKaOshCjofbhMWpHjKAo+5dhnjfwj6FcbrVy5Ek1NTepXaWmp1kMiH1J2kZ6UFQ9TpF6TMaSofV66IITQZAw0cjx3hKZ9jmXLvq53UUzIiAMgN8Yj/4nw5ZOnpKRAr9ejqqrK6faqqiqvFeMajUbWxoSRnafl4GVWXoJmY1A67HZ229FusSHG6NOPEfkIzx2h6fWv5CD0ksn+WfAxMVPu9XKoosUvr0cyn2ZeDAYDioqKsG7dOvU2u92OdevWYd68eb58aQpRSr3LrDxt6l0AINqgV4v0OHVEFDiqmztxrLoVkgRcOSPLL6853pF5OVrN4MWffH7JeM899+Dmm2/G7NmzMWfOHDz99NNoa2vD8uXLAQA33XQTsrOzsXr1agByke/BgwfV78vKyrB7927ExsZizJgxvh4uBbAOi01NzWpVrAvIRXrJMUaUNXagtq0Lecn+X65NRP3tLm0EAIxLi4PZFOmX11QKdhvau2Gx2mGICPpqjKDg8+DluuuuQ01NDR588EFUVlZixowZWLt2rVrEW1JSAp2u5x+7vLwcM2fOVH9+8skn8eSTT2LBggXYuHGjr4dLAWxnSQOsdoEMswlZCb7tnDmUlFgDyho7mHkhCiDbHM0rZ+Qm+O01E6MjEamX0G0TqGntQrbG56Zw4ZfJ+hUrVmDFihUu7+sbkBQUFLAIklzacFhubDh/bIrGI+m1OSN7vRAFBKvNjnf3lAMALprovx5QkiQhNdaI8qZO1LQwePEX5rcoKAgh8PFBx35GE7RpTtebujkje70QBYTDlS2oaelCnCnC7w0sU+Pki5lqNqrzGwYvFBR+/MZulNS3I8ag16yzbm9KW/BSbshGFBAOlMtLpKdmx/u97iQ1Tj4fVLcwE+svDF4o4FW3dOKd3XI6+JqiHEQZtOnv0tuYdGWFQavGIyEiADhYLhfzT84y+/2108yOzAuDF79h8EIB74tjdQCAtDgjHv7GZI1HIxubFgsAOFbdyhotogBwwBG8TNIieHFMG9W0cNrIXxi8UMDbelIOXq6amQ1J8n27b3cUpsRAJwFNHd2oYdEukabsdqG2UZic5f9tQ9Ic00Y1zLz4DYMXCnh7SuW57JkadtXtyxSpV9uPH6vi1BGRlk7Xt6PNYoMxQodRKTF+f30l88JpI/9h8EIBrbPbhq+r5M6VU3MStB1MH2PS5LqXYzUMXoi0tN+xn9GEjDhE6P3/Z61ntRGDF39h8EIBbevJeljtAqlxRmTF+3Z7e0+NTZfrXo4y80Kkqb1nGgEAU3O02WleKditbe2C3c4aOH9g8EIB7cO9FQCASyalB0y9i2JMqiN44Z4mRJrac0bOvEzTKDubEmuETgKsdoFK9nrxCwYvFNA2H6sF4L8dYj0x2rHi6ERNm8YjIQpfNrtQp42maxS8ROp1mJAhr3JS9lci32LwQgGrtL4dZY0d0OskzNZwI8aBjEqVCwOrW7rQ0tmt8WiIwtPxmla0W2yINugxxnFBoYXZBfI5avupBs3GEE4YvFDAWv3RIQByx8wYo1+24fKI2RSprjI4zuwLkd/Z7ALX/OELAMCUrHjoddpNLSubQe53dPol32LwQgHpUEUzPtxXCZ0EPLBkotbDGVBBspx9KeE2AUR+9/mxWrR0WQEAC/24GaMr4zPk1YdfV7WwcaUfMHihgPTy5ycBAJdOycScwiSNRzOw7ER5B9nyxg6NR0IUft7fK28bUjwxDXecP0rTsYxOjYVeJ6GxvZv9XvyAwQsFnNYuq7qX0S3nFWg7mCFkJcjLt8saGLwQ+dtexyqjb83O1Xw1oilSj4JkuXHlQUe3X/IdBi8UcA6WN8NitSMz3oRZeYFXqNtbdoJ8smLmhci/uqw2HHNsjDo5W5v+Ln0pq512lzRqOo5wwOCFAo6ytf3kLLPmV1NDUTMvDF6I/Gp/WTOsdoH4qMiAaWA5w7GFyTPrjmJXCVcd+RKDFwo4+xw9GyZpsMGap5SC3ZO1bbCxsyaR37y69TQA4PxxqQFzkXP+2FT1+ze+KtVwJKGPwUsIstrsQVvt3tltw6cHqwAAZwdwoa4iNykapkgduqx2rjgi8hOL1Y7/7K8EACw7J1/j0fQoSInBb745DQCw6euaoD0PBwMGLyFmy/E6TPjFWjz/2XGth+IRIQR+8c5+TPjFWjR3WpEVb8LZo5K1HtaQ9DoJYx0bNB6p5DYBRP7w1al6tFlsSIk1YmZuYNXFXTEtCxE6CeVNnShv4lYBvsLgJYRUNXfihv/3Jax2gd+sPRJUUf+mo7X465en1Z9/smg8dBo2nPLEuPSe/g5E5HsbDlcDAC4Ynxpw54kogx4TMuVzAgt3fYfBSwj5/fpjTj8rtSPB4O9bS9Tv//eyibhqZraGo/HM+Ay5JfkRBi9EPieEwHpH8HLheG0b0w1E6bbLol3fYfASQpRlg4qPD1RpNBLPdFlt+O/RGgDAv1ech9vmjwqYAjx3jFUyL5w2IvK5t3aW4URtG0yROpw3NkXr4bh0VoFcr/fF8TqNRxK6GLyEkNN18v46N82TC9iUq5NAt/dMk2P+2oDJWWath+Ox8Y7g5URtG5o6uEEjka80tXfjoXf3AwB+eNFYxEdFajwi184dIwdVByuacaahHd02u8YjCj0MXkJEZ7dNLQ67tigXgFyD0WW1aTkstxwsl7tRTs9JCLj5a3dkxpswPj0ONrvA37eVDP0AIhqW/eXyhU6G2YTvarwdwGBSYo04y7HL9Hm/3oBr12xhKwUvY/ASIkody3TjjBGYkm2G2RQBq13geHXg73asBC+TgjDrAgCSJGHZuQUAgPcc2xoQkfedrpPPcxMz4xChD+w/Xz8uHqd+v7u0EesOBcc0frAI7H99cpvyoc5LjoYkSZiQKQcCh4Jgj409ZxoBAJMygzN4AYBFkzOgk+Q0cSn7vRD5xOl6+WIs39EcMpDN7dPq4bOvazQaSWhi8BIiTjnqXZSOr0ogcLgyMIMXIQT+ub0UT318BIcrWyBJCIq+LgNJijGoRXofH+QVFpEvlDgu0nKTojUeydD0Ogk/uGC0+rNygUneweAlRCgfjHzHrqYTMuQi0kMVgbkC5ssT9bjvzb34P8fy7pm5CUiMMWg8qpFZNDkDANQOwUTkXYcdK/pGpQZ+5gUA7ls0Hq/fcTYAeQsR8h4GLyHidL1z8DIxwDMvf+vVkC45xoBfXD5Jw9F4x9xRcublUGVzUDUIJAoG1c2dOFnbBklCwO82r5AkCaNT5T5QZY0dqGpmx11vidB6AOQdZxoc6dREOXgZlx4HnQTUtlpQ3dKJtLjA2HUVAGx2gU2Ovi5vff8cTM2OhyEi+OPo0amxkCSgsb0bdW0WpMQatR4SUcj48mQ9AHlKPFCXSLuSEmtAdkIUyho7sOzlr1CQHI1IvQ7PXD8jqPpZBZrg/4tBEEKgvLEDAJCdGAVAblFdkCKnVgNt6mhfWRNaOq2IM0VgRm5CSAQuAGCK1CPH8fvv2zCQiEZm6wm54dvcwuCqjZMkCb//9kxE6iUcqmjGR/sr8d6ecpzgNNKIhMZfjTBX32ZBZ7cdkgRkxPdkWNSpo4pmfLSvAste3ob9AbBlwCcH5d1g549NgT4I+7oMZowjRczghci7tjoyL8r0bDCZmZeISxw1cYrVHx7WaDShgcFLCChvlOdRU2ONMEbo1duVbrX/2F6K77+6ExuP1OClz09qMkZFbWsX/rH9DICeAtdQMiaNwQuRt9W2dqmfqTkFwRe8AP1bQXx6qArbHAEZeY7BSwgoa5TrXbISopxuv3hiOgDgeE1PenLrCW0/LC/+9yRqWrqQkxiFYsf4QokSvByvYfBC5C3KH/kJGXFBuyrxG9OzYIp0/pN7+1+2480dZzQaUXBj8BICyhyZF6XeRTE2PU5dMt1zbAeqNax4V6aMfrp4AmKMoVcvzswLkfetOyTv0xbMvaByk6Kx/t4LsOehS/DwNyYDAJo6uvGTf+5BQ5tF49EFHwYvIaCswVGs2yfzAgAXTejZMl6J+iuatAleqps7cbymDToJuGB8qiZj8LUxaXKwWNHUidrWLo1HQzQ8j/z7IC753Wf4aF+F1kNBu8WKDx3juGJ6psajGZmshCjER0U6nZcB4KtTnD7yFIOXEKCuNHIRvNw2fxTOH5eK1VdPxTjH7sc1Ldr8UVWyEfnJMTCbgmepoyfioyIxLl3Ovmw/1aDxaIg8V9HUgZc+P4mvq1rx1Cdfaz0cbD/VgI5uG7ITooKmv8tQ+nYI3sraF48xeAlQR6tasPzlbdhxeuj/1OVNcvDSt+YFkNvW/+WWObhhTh5SHX1HajTKCBxz1IGMDpLumMNVlC8XFCp7NhEFkw/29mRbjtW0oqmjW7OxtHVZcdNL2wDIq4xCqS/KjxaOVb9n4a7nGLwEqP/33xPYcKQG1zy/BZ3dtkGPVaaNshIGb0SXGucIXjTKvBx3ZF5GO+pCQlWa4/fc0qndSZ9oOFq7rFjz2XH1ZyGAXSXaZRA/6DVtdf7Y0JpqvnvhWHzy4/MBAAfKm3i+8BCDlwBV36uAa++ZgXuzdHbbUOc4Nidh8M3KlD+q1S3a1LzsdfSY6VtEHGqUpnsWq13jkRB55k//PYnaVgsKU2Jw8aT+qxX9bbujFiQ/ORrfmJ6l2Th8QaeTMDY9DnlJ0bALYPtpTjN7gsFLgGpo74nCjwyyP1GZo94lxqCHOWrw1TtK5qW62f+Zlw6LDfscQdjs/ODs0+Aug57BCwWnrSflLra3zx+FsY4M6ek6bYIXu13g82PyeB68fBJ0IdbQUjG3UD4fcurIMwxeAlSFIygBgF+8ewCrPzqExvb+y+l6bwsw1Hyw0h+hsd3/6cmDFU2w2gXSzUa1hX6oUjMvNgYvFFyUjG9OYhQKkuXatFOOHev97YvjdShr7ECcKQLzRgfvEumhzHUs/95wuJobunqAwUsAKq1vR3mf5cx//OwELnxyI1q7rE63K8GLq2LdvhKi5OBFiwK8yiY525OXFB1SRXeu9Ewb8UREwUWZgk6KMag71J+s1aZnkZIFWjw5A9GG0OsJpSiemAZDhA6HK1sGLREgZwxeAogQAjtLGnD/v/a6vL+hvbtf8VxPsa4bwUu0vDy5scP/DZGUnifhsNNypJ6ZFwo+Qgi1WVpyrEFtuHimoQNtfS6a/OGM49w2KjW0C/wTog24aLzc92WLY/NJGhqDlwDy9KdHcfUfvlDnee84fxTWfKfI6Zh9fTZWVLvruhG8KNvIazFtpKxwUupuQllP5mXwVWJEgaS5wwqrXc4WJsUYkBxrRFqcEUIAR6r8vzP9mQZ5uio3KbSnmQFgak48AOBA+cD1jeSMwUuAsFjteGbdUafbfnjRGCyekoGb5uWrt+0qaXQ6RtnXyJ3gRcm8dFntQy6/9rZwyrwoBbvdNk4bUfCoa5M/o7HGCHWD1wmOzQTf3lnm17G0dVnxlaPJY07i4KsoQ8GUbDl42V/GaSN3MXgJENv7NKPLT45GnKML7QNLJuKei8cBAL48Xgdrr+mI8gH2NXIl1hgBvaNi3991L+EUvBi5VJqCUH2vehdF8UR5OuP1r0r8es548uMj6vfuXJgFu+k58dDrJJysbeO+aG5i8BIgNn1d6/TzlKx49XtTpB53XjgG8VGRaOmy4r9H5WPtdoGKQbrr9iVJkmZTRzWt8okxJTY4d4T1RCSXSlMQUi4wknt9Rv/nbDnr220Tft08cKej50lqnDEszhkJ0QbMKZCXTBc/9Rm6OOU8JAYvAeJghfNcZ9/lxHqdhGtm5QAAfvepvN9ITWsXum0Cep2EdDdrSRLU4MV/J6KWzm4ccszljgrxrQEALpWm4FTl6P+UYe7p1C1JkvpzS6d/inaFEDjqyD78/fa5Ib86UXHreYXq95UabZ4bTBi8BAildX5anBHxUZFYdm5Bv2O+f8FoAHLH3bd2nFEb1GWYTYjQu/dPGa+uOPJf5mX94WpYbHaMTo3B6BBfOQCwwy4Fp8pm+Q9mutl5m5E4k7xMuaXLP+eM8qZOtFtsiNRLyE8O/YsdRfGkdHVBQ7uFmZehhO7i+SDSbrGqgchHd81HjDECpkh9v+NS44yIM0agpcuKe/+5B79cOgXA0Hsa9aZkXvw5f61sZ79kamZYXEUZuFSaglDVAMFLrBK8+Cnz8rVjZVNhSow6BRsuog3yeb/d4v+l6cEmvP5nBCilQCsxOhLJsUaXgYvi5nMK1O/f31sOwLOCNqXmpclPNS+l9e3YcKQGALB4SoZfXlNrhgg5QGPmhYJJT/DiPAWtLBxo9VPwcqxKPh+OTQvtPdBcUZrxMfMyNAYvAWDrCXml0YzchCGP/fHF43DuGLmd9JeOx7lTrKtIiHZsEeCnRnX/t+4oLFY7zh6VhEmOZZehzqCXg89uZl4oiLiqeQGAOKOSefHPBY+SeRmbHvpTzH0pmZe2LgYvQ2HwEgC+OC6vHjp3TMqQx+p1EpbOyHa6zZ1l0gp/rjZq6ezGvx3ZofsWjQ+LKSOANS8UnOrU1UZ9My9y8NJ3axJfOVYjZ16UDr/hhNNG7mPwEgCUlUYz8xLdOn7B+FSnny9wtJZ2h9Kozh81L19XtaKz244Mswmz3HxvoUAJXqx2Abudjeoo8NnsQi3iT4yJdLov1ujfmpcKR++qvKTQb07XV0/wwszLUBi8aKy1y6qma8e4uRInLc6EiyelAwBumpc/vJoXPwQvNS3ySSgrwRQ2WRcAiNT3vFcW7VIwaO7ohrKhcWK0c18VpealxQ+ZF5tdoMaRAepbOBwOempemHkZil+Cl+eeew4FBQUwmUyYO3cutm3bNujx//znPzFhwgSYTCZMnToVH374oT+GqYmTNW0A5OZtyjJmd7zwP0XY//AiPHLlFI9eT92c0Q/TRuG0n1FvSuYFYPBCwaHe0fcpzhTRb4WPP1cb1bV1wWYX0ElAckzoN6fry9PMy+m6NqcLUesIzjdC9GSJ+2aMhRB4bWsJvjol11keKG/C/rKmfv1oNh+txbPrjqLbZkd9mwVCCKfn9SafL5V+4403cM8992DNmjWYO3cunn76aSxatAhHjhxBWlr/6Y4vvvgCN9xwA1avXo3LL78cr732GpYuXYqdO3diyhTP/lAHg0OOKaNRKZ7N70qSpKZzPREf5b+C3WpH8JIWF15XUIZeJ3/WvVAwUJpW9s26AL1qXvxQsFvd3HPB427vqlDSO3ix2wVO1LaiIDkGx2paER8Vicz4niz7lyfqcOOLWxFrjMBVM7PxdVUL9pQ24tGlU3C1o6EpIAcev/v0KP742XHcXTwO152Vi31lTdh3phH7yprQbrGhvs2C03Xt+Mkl45AYY8Bdr+8GACyckAZTpB6fHqpCl+NcNiEjDocrezbqnJRpRprZiIPlzeo5/7effK3eb4rUYWKmGf972UTMykv0Whbe58HLU089hdtvvx3Lly8HAKxZswYffPABXnrpJdx///39jn/mmWewePFi3HfffQCARx99FJ988gl+//vfY82aNb4ert/9Y3spAPeKdb2BmRffkyQJBr0OFpsdHRbbiK6GApEkSeoeWRQa6tuUehcXwYsfa16UK/lwnDICeqaNXth0Au/vKUd5r8yGJAEXjU/DDxeOxT+2l+IfX5XCZhdo6ujGK1+cUo+75x978PO392N6bjxqWrpw3JHdB4Bfrz2MX689PODrr/r3Qaef1x2u7ndM78AFkGs2D1YM/J46u+3YVdKIa57fgpeXn4ULPajRHIxPgxeLxYIdO3Zg5cqV6m06nQ7FxcXYsmWLy8ds2bIF99xzj9NtixYtwjvvvOPy+K6uLnR1dak/NzcHz5bi7RYrtjv28PjWWTlDHO0dSpO6lk4rbHbh0z9C4Rq8AHLdi8UGzP/NBq2H4nXfnpuHX101VethjFgwnzu8Tdm3KNHF1LVa8+KH4KWkvh1A+AYvUYaeHl/lfaZkhJCDCVcBRV8d3Ta1lcZAEqIj0djejfioSFw4PhXv7C53uv/yaZkQQp4iijVFICpSj5RYI9LijEiJNeJ7F4xGRWMn3t5Vhi+O12JiphnfOTsfb+86g71nmrD5WC0unpiOSyZn4MsTdfjqVD3O8+JFuk+Dl9raWthsNqSnpzvdnp6ejsOHXUd/lZWVLo+vrKx0efzq1avx8MMPe2fAfnbCEREnxRic0oG+ZI7qOTk1d3S7vNLyloom102vwsG5Y1Lw8cEqrYdBgwjmc4e31Sk7SruYNor141JppabCnZ5XoWighRS/vXY6ZuYl4L4392LH6QbER0XiN9+chuKJ6Wi3WKFzTMW0W2x4dv1RnKhpQ7rZhLd2nkFeUjT+fMscJERFoqyxAyv/tQ9Lpmaq280ofnfdDGw5Xof95U24YU6eGrQOJi85GncVj8VdxWPV2+5bNKHfcd8sykG3ze7VjslBvz3AypUrnTI1zc3NyM3N1XBE7juu9DPw434/kXodYo0RaO2yotGHwYsQAqWOq6jcxPBb8vjH/ylCc0dorhiIjAiNKaNgPnd4W1mj/Fl11fBSqXlp9nHNi9Vmx9aTcvAytzDJp68VqK6emY2/bTmNydlmPHbVVLR2WnG0uhVXz8qGJEn4x3fnYX9ZEwpTY2B2BBe9g4wYY4TTIo7ffHMahBBq/VBijAH//uF5Ll9bkiScMyYF5/iohMHbWz34NHhJSUmBXq9HVZXzFWhVVRUyMly3is/IyPDoeKPRCKMxOK/slc0YR6f5d/Ox+KhIOXhptwDwzWs3dXSrSytzwjB4kSTJo9Vj5H/BfO7wttJ6eW+1vrvZAz01L61dVgghfNb24LOva1DfZkFidCSm5ST45DUC3dj0OOxddYnT73h6ryyUXic5/TwUuSwgNC42+vJpObfBYEBRURHWrVun3ma327Fu3TrMmzfP5WPmzZvndDwAfPLJJwMeH8zKHM2Y/P3H3R+N6pS567Q4o9M8LhEFnjMNjiypi8ZwypW9EECbD5unKTUXV8/KcWo3EG7CqSfWSPh82uiee+7BzTffjNmzZ2POnDl4+umn0dbWpq4+uummm5CdnY3Vq1cDAO666y4sWLAAv/3tb3HZZZfh9ddfx/bt2/HCCy/4eqh+V+1o4pbm54JWfwQvypWcq5MhEQUOIQTONAyceTFF6qDXSbDZBVo7rcNq0TCUbpsdG4/IhahLpmZ6/fkp9Pg8eLnuuutQU1ODBx98EJWVlZgxYwbWrl2rFuWWlJRAp+uJss855xy89tpr+N///V888MADGDt2LN55552Q7PGirMZJ83NlvT/2N6p07FCbER+eqwaIgkVDe7faw8PVwgFJkhBnikBjezdaOrt98pk+UtmClk4r4qMiw7ZYlzzjl4LdFStWYMWKFS7v27hxY7/brr32Wlx77bU+HpX2epq4+Tfz4o8tApSsUnqYNagjCjb1bT3ddQearok1OoIXH604Uor7R6fGsIcQuSV8JxY1ZrHa1ZOGv/ugKHPYbT5c+ljTrGSVWBBJoUcIgV+8sx9/2nxS66GMmHIeGqwdv3LOaPbRBc9pR/ASjpsx0vAE/VLpYFXr2HwsQie57K3gSzGOLo6+3GhNq6wSkT9sP92Av355GgBw87z8oG5lrwQvg7VNiI+Szxm+ytaWMHghDwXvJy7IHamSWyznJUVD5+c0qdp0yocdM6ualWJkThtR6BBC4N3dZbh2TU+HcHc6nmrlpc0n8T9/2qoWw7riTuZFmWr2VeZF7QnF4IXcxOBFI3tKGwFo00myd98GX1GDF04bUQipbunCz97a63Tbd/+6A698frLfTrxasdsF3tlVhmvXfIFH3j+I/x6txXf/ukNdDt1XwyCbMip8XSd3uo6ZF/IMgxeN7DvTBACYlhPv99f2dealqb0bzZ1Kgzr/bHtA5A/pZhN+6qL9+ap/H8Tznx3XYET9/WtXGe5+Yze+OtWg3tZltePOV3di64m6fsfXtTq2BojVJnix2uwoa5SXaucn+7dhJwUvBi8aUT6shX7cGkCh9GnwVc3LqTp5z6a0OKO6SypRqFh2TgHmuGhf/8ynRwMi+/L2rjMub99zpgn/86dtalZUoWReBqu9S3Dc54vgpaKpEza7gCFCxxo5chuDF41oWdDas9Gab1LASvBSwKsoCkE6nYS/3ToX/15xHv73sonIdPQ9sdjsOOn4v6+V3g3nFC8vPwvzRiUDkMf4yPsH0W2zq/ermzIOUvNi9mHmRZkyyk2M8nv9HwUvXhZroPcyaS22fldrXnw0bXSqVj4Z5Sdz/ppCkyFCh6k58ZiaE4/b5o/CtWu+wFenGrDjVANGa5BNVby544waDKy9ez7Gp8dBkiRcMC4Vnx6qxvf/tgMf7K2A2RSB1VdPAwA0uBG8+HLaqNyRhc4Owz3QaPiYedFAjWOZdKReQqIGm/f13uJeCO+nuZXdsgtTmXmh8KBkNtYeqNR0HG98VQoAuG/ReEzIMKv75EiShIsnpeOJa+WA5a0dZY6NWXtWG7kXvHj/gkfpxp2pwYUcBS8GLxq47c/bAQDdNt/t0DoYpeal2ybUtuDedMyxW/bYtDivPzdRIPrGjGwA8s7Iv/vkaxQ9+gl2lTQM8SjvKq1vx/bT8mteMyvH5TFLZ2RjfHocLDY7Nh2tBeBe8BLnuOBp6fR+5kWpwUnnykTyAIMXP7PbBQ5VNAMARqVok5mI6VVE6+3l0ja7UDMvY9K0S58T+dOYtFiMSYuFzS7wzLqjqGuzYMVru/w6hpc+l7v9zh+bMuD+Q5IkYXK2GYAc7HRYbOjolneKHix4US54fNGVu8rRjTud+6CRBxi8+FlVS0+l/6+unqrJGHQ6ST0ZebvupayhA11WOwwROuRymTSFkfPHpjr9XNbYgWYfZCoGstmRSblxbv6gx+U6akvONLSj3jF1FKmXBt0tOkYNXmzeGKoTNfPChpbkAQYvflbiKKYrSI7G2Y55ci3E+qhR3dFquXPwqJSYoG6ZTuSpuaP6L5/+urLFL6/dYbGpGc+ZeQmDHqv0XjrT0IHnNhwDIDeoG2wKO9aRrbXY7LB4eaq5ook70JPn+NfFz04HSBvs3kW73qTUu3DKiMLN9JyEfrcd8lPwcqiyGXYBpMQahmy/oJx7dpc04s3tck+Yy6ZlDvqYGKNe/d6bU0d1rV3qPm8FGk2jU3Bi8OJnpQGyAVmMj6aNWKxL4ap35mC0Y6Xd7pJGv7z2WzvkIGR2ftKQiwDGpcchUi+hpcsKi82OKdlmPHTF5EEfE6HXwRgh/7nw5gXPQUf9X0Fy9KDTVkR9MXjxs0DZPdVX+xuVOvZPKUhhzwYKP6/fcTZ+cfkkPHLlFADAxiPVsPmg627vFgdCCLy/twIAcNO8wetdALkw99rZuerPSx0rpYaiFu1avHfOOFAuBy+Ts/y/TQoFNwYvfhYowYuvtghQ9klJjeWyRwo/Z49Kxq3nFWJOYRLiTBGoa7PgoOMPtLd0dttw9fNf4Mrfb4bFakdVcxeaOroRoZMwu6B/3Y0rd144Rv3+4knpbj0mxgcrjpTl5FOyGbyQZ5in87NA2frdV5szKvPXyQxeKIxF6nWYlZeIz76uwc6SBkz14gasf9h4HLsc01FfnaqH1ZHZKUyJgSHCvevR7IQo/OO789Bmsbq9GWK0Qa578daKIyEEdpxuBADMLkj0ynNS+GDmxY/auqyodWQm8jRund+z2sh7Szm7bXY0tMvPlzLIDrVE4WBWnvwH+T8HKr3WybrDYsNftpxSf153qFrN7IxL96zObE5hEi4cn+b28d7u9VLTIhfr6iRgKjMv5CEGL350slbetC0pxgCzyf/bAvQW54PMi9KpUyfJSy+JwtlFE9IgScAXx+vwuqNt/0jtON2AxvaeC479ZU349FAVALjc6dqbYrxcJ6f0mEmINsAUqR/iaCJnDF78SOnDoFVn3d58UfNS49gpOynGyN1hKexNzYnHPcXjAAAr/7UP6w9Xjfg595U1Aeg5h2w7VY8dji0BFk/JGPHzD8bbmZcmRxCWEKXthRwFJwYvfnSiRs68aLnrrMIXNS91jswLp4yIZLfNH4UoR1Zhw+EaAMC7u8vw7u4yj5/rZG0bfr32MADgyhnZ6H19MCo1xuc71Ec5al7au71T89Lo2KE6XoPNaSn4MXjxI2XaKBB2W/ZFh91aR+YldYgmWUThIsqgx0NXTAIAfHmiDqdq23DX67tx1+u71fOBuz7aX6F+f8nkdKeLIFcN8rxNKdjttHgneGHmhUaCwYsflTV2AOjZW0RLcT7osKusNErhSiMiVY7j8360uhUXPLlRvV1pLOcupTD39vmFmJhpxvcvGK3e54/VOkoGqcNrmZeemhciT3GptB9VOvbwyEzQfg+PWKN8tePNaSN1mfQgu9MShZucATYo3X663qPnUYKX+Y4NIK+elYPUOCPONHTgmlk5IxukG0zeDl4cmZd4Zl5oGBi8+InNLlDp2D01MwA2IPNFwa6yDDyF00ZEqr4XK6NTY3C8pg2HK1sghBiynT8ANLRZcMIxzdS7odv8PjtZ+5Ja8+KlaSO15oXBCw0Dp438pKalCza7gF4nIS0Atn5Xpo282S2T00ZE/Rkj9HjsqilYMjUDq6+eig9+NB8ROgmN7d1Y+NvP1CLcwWx3rCgakxaLJI0ym2rNi5cyL03MvNAIMHjxk/Imud4lPc4IfQAsI1YyL+0Wm9f2XlEzL1xtROTkxrn5+MONRbhhTh5MkXqcPSoZAHCitg3PbzyOakdWdiDKFNNZbrb/9wV12shLmZcqx3tOM/NihzwX1sHLjtMNeG1rCapb5A/R79cfxbmPr1db+HvT0aoWAHC7FbevxfTawdVbRbs1jt8jMy9Eg3v2hpm4fX6h+vPmY7WDHn+4Qj5/TMk2+3Rcg/F2wW6FUgMY77omiGgwYRu8NHd245ZXvsIDb+/DnMfW4Z/bS/Hkx1+jrLEDb3ipG2ZvB9XdU7U7+fRmiNCp+6B4I3jp7LapmZfsBJ6MiAaTGGPAzy+bhDsvlFcMDXXOOVIpBy8TMjzbAsCblGkjb2ReetcAZgXAAgYKPmEbvPz581No6uhps33fm3vV7880eD/zom79ruGVU19xRu81qit3LAOPNuiRwKZTRG75ztn5iNRL2HqyfsC+L43tFvUPvaf7F3mTNzMvgVYDSMEnbIOX6bkJmJYTjxm5Cf3u2+/lLeztdoFDFUrmJXA2IFO77Hphc0alh012QpRbqyeISJ4yGZsmByRHq1pQUtfebxPHd3bJ3XjHpMUiTsM90UwG7wUvyvkiw2wKiBpACj5hu1T6/HGpmD82BUIAj689jBc2nVDvU04g3voj/Octp9BmscEYoQuIfY0U6nJpL2ReyhocwcsAPS2IyLXClBgcrGjGA2/vQ22rBf9zdj4eXToFALCntBGr/n0QALBwgvs7QPuCmnmx2Ef8XMo+aCzWpeEK28wLAEiSBJ1OwspLJ+Cf35uHf/3gHACAxWZ32rl1JHaXNuJhx8knOcaACH3g/Mq9uUWAciWVxXoXIo8UOi5olJqxv355GqX17ejstuFnb/VMZ193Vq4m41P01LyM/HxR16Y0tGTwQsMTtpmX3iRJUpcgJkRHorG9G9UtXUj0Qj+FVz4/qX5/3Vl5I34+b4rz4uaMvaeNiMh9hS6ysTtON+BkrdzITpKAtXedj1Eab+jau+ZlpJnpOrZVoBFi8NJHepwJje3dqGruxHgvVPYrzaWe+/YsLJnq2y3rPeXVzItj2migVuhE5NqC8f275N79xm71+x9dNNYr56KRMjqCF7uQs9PGCP2wn6tO2UqEwQsNU+DMYQQIZQ62aoimUe6oau7EmYYO6CTg/HEpAVfIqhTseqXmhZkXomFJiTXitvMKYYrUYfHk/hc418/RdrpIYYzo+XNhtY2ssWVdm5x54bQRDReDlz6UZXvVLV1oau/Gfw5UwmIdXoHaDkfWZXyGWdNVAgNRN2ccYebFZhfqppOseSHy3M8vm4hDjyzG89+ZhZl5CertPy4eFzBN3CJ71esN95yoUKaNmHmh4eK0UR+FKfL29U/85wie+M8RAMBvrpmGbw2jWE4JXmbn+367+uHwVs3LmYZ2WO0Chggd0s3s2UDkqd5Z2b/cMge1rRbkJUUH1DJivU6CTpKnjbptIwxeWLBLI8TgpQ9XfVgOVgyv78uuEjl4mZWfMJIh+Yy3al6+rmoFAIxOjQ2oky1RMIozRQZkphaQsy9dVjssIw1emHmhEeK0UR+uOuDWOIrLPGG3C7Wl95QAakzXm/eCF/l9jkvXdjUEEfmWsqVI9whqXmx2gfp2Bi80Mgxe+kiLM+Eb07Mwb1QyfnfddABAhaMY1RNljR1os9hg0OtQEECN6Xrr6bA7suDlqBq8aL8igoh8x+CoexlJzUtjuwVKE+GkaAYvNDycNnLh/26YCaBn2kfZ/dQThx1Zl1GpMU6FboEk1kt7GynTRgxeiEKbci4bSc2LstIoIToyoJp2UnDh/5xBKCtnqpo7Pf6wbjhSDQBOKwcCjTemjWx2gWM1SvDCaSOiUBYZIde0jaTmpVbp8eKFJqAUvhi8DCI11ohYYwTsAjhR43rHV1f+e7QGr20tAQAsctG3IVD09HkZ/lYIp+vaYLHaYYrUITcx2ltDI6IApGZeRjBt1FOsy5VGNHwMXgah00mYmClPhSx6ehM2H60d8jFWmx2/eGc/AGBuYRLOHZPi0zGORFyvzEvfnWzdpTSny0uKho4rjYhCmlrzMpJpI0fmhVsD0EgweBnCmLSeOo5fvLt/yOP3lTXhVF07zKYI/GnZWQFb7wL0ZF7sYvjb3NezUyZR2OhZbTT84IXnDPKGwP3LGiC+WZQNU6T8azpZ24aKpsFXHu0saQQAnFWQpNaUBKqoSD2UZMlwi3aVE1ES56+JQl6kutpo+Eula3nOIC9g8DKEovwkHH70UkzPkXu1vLn9TL9jum12PPXxEby0+SR2qo3pArOrbm+SJKkBVsswi3YZvBCFj0i9fLUzotVGnDYiLwjs1EAA+c7Z+djz5l48/9lxLJmWia0n6jF/bApyk6LxzKdH8fsNxwAAMQZ5p9VAXmXUW5wpEs2dVmZeiGhIBsdO0iPp88KCXfIGBi9u+mZRDn699jBqWy247P/+i85uO8ymCOx68BJ1WTQAtFls0EnA9JwE7QbrgZEul2bwQhQ+DN7IvKg1Lzxn0PBx2shNkiRh3mh55VBnt/zBbe60YuORanUbAMXETDNiArzeRdGzXJrBCxENzhtN6tQ+L8y80AgwePFA8cS0frf98bMTsNqdi9euLcrx15BGbKSZl8Z2uUdMItt8E4U8JXjpGua0kcVqVy+UmHmhkQiO9ECAuGRS/4Zz207VA5C7y07NToDNbsf1c/L8PbRhU/c3GmajOiXoUZ6HiEJXT+ZleKuNlEytXichPiowd86m4MC/OB6IMuiRHGNQ52x7m5mbiF9/c5oGoxoZpVFdm2V4fV7aLXLwohQqE1HoGmmfF2XKKCnGwKaWNCKcNvLQRRN6po6UDzIA5CUHZ2t8dan0MGte2rrkoCdYanyIaPhGWrDLYl3yFv7F8dCqb0xGZIQOV0zLwm8/PoLtp+W+LvlBGrzEqDUvnk8bWax2tU14jIH/lYhCXU+TumEGL2qxLoMXGhmfZV7q6+tx4403wmw2IyEhAbfeeitaW1sHfcwLL7yACy64AGazGZIkobGx0VfDG7YYYwR+ddVUzBudjPPG9uxbVBQETelciVNrXjzPvChTRgAQbeS0EVGoi4wY2d5G3BqAvMVnwcuNN96IAwcO4JNPPsH777+PTZs24Y477hj0Me3t7Vi8eDEeeOABXw3Lq75zdj6unpmN126bi8z4KK2HMywjWW2k1MkYInQBvYcTEXnHSJdK16oN6ph5oZHxSa7/0KFDWLt2Lb766ivMnj0bAPDss89iyZIlePLJJ5GVleXycXfffTcAYOPGjb4YltelxBrx1HUztB7GiIykz0ubstKI9S5EYcEY4Z1poxT2eKER8snl8pYtW5CQkKAGLgBQXFwMnU6HrVu3evW1urq60Nzc7PRF7htJ5kV5TDRXGlEQ4rnDc0rwMtw+LyzYJW/xSfBSWVmJtDTnhm4RERFISkpCZWWlV19r9erViI+PV79yc3O9+vyhTq15GUbw0u5YacTMCwUjnjs8pwYv3SPLvLAjN42UR8HL/fffD0mSBv06fPiwr8bq0sqVK9HU1KR+lZaW+vX1g12sUW4UNZyCXWZeKJjx3OE5o2Njxi7r8PpCqZkXThvRCHl0yXzvvfdi2bJlgx4zatQoZGRkoLq62ul2q9WK+vp6ZGT071I7EkajEUYjPwjDNZKaF7VBHTMvFIR47vCcMXKE00aOgt0UFuzSCHn0Vyc1NRWpqalDHjdv3jw0NjZix44dKCoqAgCsX78edrsdc+fOHd5IySeUaSOLzY7ObhtMke5nUZSCXfZ4IQoPPZkXz4OXdosVHd1yxoaZFxopn9S8TJw4EYsXL8btt9+Obdu24fPPP8eKFStw/fXXqyuNysrKMGHCBGzbtk19XGVlJXbv3o1jx44BAPbt24fdu3ejvr7eF8MkALGGCChdups7PGtUpyyVZuaFKDz0ZF48nzZSsi7GCB23E6ER81lzjldffRUTJkzAwoULsWTJEpx33nl44YUX1Pu7u7tx5MgRtLe3q7etWbMGM2fOxO233w4AOP/88zFz5ky89957vhpm2NP12iCt0dPgRV0qzRMRUThQCnY7h1Gwq+xrlBxjgCRxXyMaGZ9dMiclJeG1114b8P6CggII4bwz6apVq7Bq1SpfDYkGEB8ViYb2bjR5HLzIV1/RzLwQhYWRFOzWs1iXvIhtUUnNvDS1DzfzwuCFKByYIoe/VLqO3XXJixi8EOKj5ZOJx9NGFi6VJgonIynYrW1Tpo2YeaGRY/BCPZmXYda8sGCXKDz0dNgdfsEul0mTNzB4ISSo00YWjx6nrjbiUmmisKCsNurstverWRwKu+uSNzF4IS9kXjhtRBQOeveBsni4szS765I3MXihYQcv7ezzQhRWlGkjwPO6F2W1UVJMpFfHROGJwQshPnp4fV5a2WGXKKwY9L2CFw9XHCnnC7OJwQuNHIMXGn7mhdNGRGFFkqRhF+0qm78q+6kRjQSDFxpWnxe7XXB7AKIw1BO8eJZ5aWFfKPIiBi+EhGjPMy/KBmsAp42IwolStNvZ7X7mpctqg8UR7MQZOW1EI8fghZymjdxd/qisNNJJPV03iSj0KU0plYJ9dyhbiQCcZibv4F8dQkKU3HfB2msqaCi9i3W5yRpR+FBqVpQaFne0dMpZ3ahIPSL0/LNDI8f/RQRTpE5dReDu1BGXSROFJ6VmRbmAcUeLI9CJY7EueQmDF4IkSTA7po4a3eyy28qVRkRhKdZRs+JJ8KIcy5VG5C0MXgiA50W77Rbua0QUjuKGMW2kHBvH8wV5CYMXAtBTtNvsZvDS2sV9jYjCkZJtbWHmhTTE4IUA9AQvjW72emGDOqLwpEwbtXlS88IeL+RlDF4IQK+dpd3OvHDaiCgcjWTaKJY9XshLGLwQAPQU7Hq42iia00ZEYWU4q41au+TzClcbkbcweCEAnhfstqlpYE4bEYUTJdvqUc1LJ6eNyLsYvBAAz/c3Uq66mHkhCi9q5qXT/e1EWliwS17G4IUAeL6ztNJ0SpluIqLwkOjI0jZ4sJFrCzMv5GUMXgiA59NGzY6rLjOvpIjCSnKsEQBQ19rl9mNa2WGXvIzBCwHotVS6w70Ou0o/mDgTMy9E4SQlVt4LrbnTqu4UPRRlmpnBC3kLgxcC4HnNS7M6bcSTEVE4MZsiodfJm7E2eLidCJdKk7cweCEAQHxUz9WUzS6GPL5FnTbiyYgonOh0EpJi5PNFrZtTR6x5IW9j8EIAejIvQE9gMpjmDkfmhcELUdhJdgQv9W3uZl7Y54W8i8ELAQAMETpEG+SeLUMV7Xbb7OjolpvUcdqIKPwkx7qfeem22dHZLdfGMPNC3sLghVTu7m/U0qstOE9GROEnPc4EAKhqHjp46b0HErcTIW9h8EIqd3u9KCuNYgx6ROj5X4go3KTHy8FLZVPnkMcqFzvGCB0METxfkHfwfxKp4t3c36jN4uiuy6soorCUYXY/eOEyafIFBi+kUnq2DLVbbGe3sikj9zUiCkcZSual2f3MC3tCkTcxeCGVcmU01GqjDotcfBcVyeCFKBx5lnmRzyesjyNvYvBCKiV4GWqre2WlkYnBC1FYykqIAgBUtXSqmdiBsMcL+QKDF1IpJ5eWIaaNlOCFmRei8JQSa0CcMQJCAKX17YMe28odpckHGLyQSpmTHip46bQ4ghfWvBCFJUmSUJASAwD4/qs7sfdM44DHqpsyMvNCXsTghVSx7ta8MPNCFPYKHcHLsepWXPnc5wNuK8LMC/kCgxdSmVnzQkRumpWXoH4vBPD5sVqXx7HmhXyBwQup3K55UaeN+N+HKFzdeHY+LpqQpv58oLzZ5XGNjp2nE6K5VJq8h399SKX2eRki89LJaSOisBep1+GlZWfh3ovHAQCOVrW4PE7p2J3g2LmeyBsYvJCqJ/PCmhcics/Y9DgAwJEBghelY3c8My/kRQxeSNXTpM7daSPOYROFuzFpsQCAU7VtEKJ/0W5Tu5J5YfBC3sPghVRK8NJltcNitQ94XLuaeeF/H6Jwl5MoN6xrs9jQ4GJHeiXzkhDNaSPyHv71IVXv1QCD1b2wzwsRKUyReqTFGQEAZxqcG9bZ7UIt2I1n5oW8iMELqSL0OrWOZbC6Fy6VJqLecpOiAQCl9R1Ot7darFDav3C1EXkTgxdy4k7dCwt2iag3ZeqotE/mRal3MUboeLFDXsXghZzEuhO8cNqIiHrJiJd3ma5u7nK6XV0mzawLeRmDF3LiTq8X9nkhot7S4hzBS0un0+2N7ezxQr7B4IWcxLnR64U1L0TUW6qjYLe6xTnz0tjhKNZl5oW8jMELOYlzY38jThsRUW/KaqOavsELe7yQjzB4ISfu7G/U2S33gOG0EREBPcFLdbPztJFS88Jl0uRtDF7IiVLzMlDwYrXZYbExeCGiHmlmuealzWJzytqyYJd8hcELOelZbeS65qWzV+ddThsRESBnbJUApaSuZ7l0z47SLNgl72LwQk7MQ9S8KPUugNy7gYgIAApTYgAAJ2vb1NuUmhdOG5G38a8PORmq5qX3MmlJkvw2LiIKbD3BS6t6WyOnjchHGLyQE7XPywDBi9pdl1NGRNTLKDV46Zk2amKfF/IRBi/kRKl5aR6g5kVdJs1iXSLqpTAlFoBz5oUFu+QrPg1e6uvrceONN8JsNiMhIQG33norWltbBz3+hz/8IcaPH4+oqCjk5eXhRz/6EZqamnw5TOplqD4vPQ3qGPcSUY+CFHlzRqealw7uKE2+4dO/QDfeeCMOHDiATz75BO+//z42bdqEO+64Y8Djy8vLUV5ejieffBL79+/HK6+8grVr1+LWW2/15TCpl7ghal44bURErhQky9NGDe3daGizoLPbpvaEYodd8rYIXz3xoUOHsHbtWnz11VeYPXs2AODZZ5/FkiVL8OSTTyIrK6vfY6ZMmYK33npL/Xn06NF47LHH8J3vfAdWqxURET4bLjn03ttICNGvKLeT00ZE5EKMMQIZZhMqmztxsq4N2QnyTtN6naReFBF5i88yL1u2bEFCQoIauABAcXExdDodtm7d6vbzNDU1wWw2Dxi4dHV1obm52emLhk+pebHZhZpl6Y37GlGo4LnD+3KT5IClrKHDaZk0VyaSt/kseKmsrERaWprTbREREUhKSkJlZaVbz1FbW4tHH3100Kmm1atXIz4+Xv3Kzc0d0bjDXYxBD53jPONq6qiDO0pTiOC5w/uUbEtZY0dPgzrWu5APeBy83H///ZAkadCvw4cPj3hgzc3NuOyyyzBp0iSsWrVqwONWrlyJpqYm9au0tHTErx3OJEmC2XGyae7ov+KImzJSqOC5w/uyHMFLaX07GhyZFzODF/IBjyci7733XixbtmzQY0aNGoWMjAxUV1c73W61WlFfX4+MjIxBH9/S0oLFixcjLi4Ob7/9NiIjB/7PbzQaYTQa3R4/DS0+KhKN7d1qg6neOpl5oRDBc4f3ZSfKwcurW0uw5XgdACAz3qTlkChEeRy8pKamIjU1dcjj5s2bh8bGRuzYsQNFRUUAgPXr18Nut2Pu3LkDPq65uRmLFi2C0WjEe++9B5OJ//H9LSEqEqfR09q7N9a8ENFAchOj1e9POJZMK9kYIm/yWc3LxIkTsXjxYtx+++3Ytm0bPv/8c6xYsQLXX3+9utKorKwMEyZMwLZt2wDIgcsll1yCtrY2/OlPf0JzczMqKytRWVkJm61/8Sj5RrxjE7Uml5kXx47SnDYioj7OGZ2MZecUON2WzeCFfMCn69deffVVrFixAgsXLoROp8M111yD//u//1Pv7+7uxpEjR9DeLreT3rlzp7oSacyYMU7PdfLkSRQUFPhyuOSgFNgpBXe9sWCXiAYSoddh1Tcm40B5E7461QCgZyqJyJt8GrwkJSXhtddeG/D+goICCCHUny+44AKnn0kbSjdMl5kX9nkhoiEUJMeowYuyYSORN7HHO/Wj7EPiKnhRa144bUREAyielA6dBNw4Nw/j0uO0Hg6FILY9pH7i1WmjQYKXCMa9ROTaoskZOPjIYhb2k8/wLxD1k+go2K1vc1Hzwj4vROQGBi7kSwxeqJ/UOLn3RW1rV7/72OeFiIi0xuCF+lGCl5qW/sELVxsREZHWGLxQP0rwUt9uQbfN7nQfC3aJiEhrDF6on8RoA/Q6CUL0r3vpsDia1DHzQkREGmHwQv3odRKSY+Si3b5TR6x5ISIirTF4IZfSzPLUUXVLp3qbEKKn5oXTRkREpBEGL+RSamz/ot1um4DNLndA5jJIIiLSCoMXcsnViqNOa8/mmJw2IiIirTB4IZdcBi+OBnV6nYRIvaTJuIiIiBi8kEvqtFGvRnW9e7xIEoMXIiLSBoMXcik1zgTAOfOi9njhlBEREWmIwQu5pKw2cgpe1H2N+N+GiIi0w79C5JIybVTtIvPCYl0iItISgxdySSnYbbfY0NZlBcAGdUREFBgYvJBLMcYIRDsa0SlTR8rWAKx5ISIiLTF4oQGpy6UdK45YsEtERIGAwQsNqG+XXda8EBFRIGDwQgPqu+Ko08J9jYiISHsMXmhAA2VeOG1ERERaYvBCA+q7RYCy6iiamRciItIQgxcakBK8VLd0AgAa2i0AgMToSM3GRERExOCFBtR3tVFDezcAIDHGoNmYiIiIGLzQgFJjnfc3alQzLwxeiIhIOwxeaEDKaqPaVgvsdqFmXhI4bURERBpi8EIDSooxQJIAm12god3CzAsREQUEBi80oEi9DkmOQKW6pQuNSs0LgxciItIQgxcalFK0e7K2DVa7AMBpIyIi0haDFxqUErzsK2sCAMSZItikjoiINMXghQaldNndeqIOAFCYEqPlcIiIiBi80OBSHSuOdpY0AmDwQkRE2mPwQoNKizM5/VyQzOCFiIi0xeCFBpWfFO3089j0WI1GQkREJGPwQoMq6DNNNCsvUaOREBERyRi80KByk6LU7yN0ErISogY5moiIyPcYvNCgjBF6jE6Vsy+3zi/UeDRERERAhNYDoMD3zPUzcaSyBVfNzNZ6KERERAxeaGhTsuMxJTte62EQEREB4LQRERERBRkGL0RERBRUGLwQERFRUGHwQkREREGFwQsREREFFQYvREREFFQYvBAREVFQYfBCREREQYXBCxEREQUVBi9EREQUVBi8EBERUVBh8EJERERBhcELERERBZWQ21VaCAEAaG5u1ngkROFL+fwpn8dgwHMHkbY8OW+EXPDS0tICAMjNzdV4JETU0tKC+Ph4rYfhFp47iAKDO+cNSQTTpZEb7HY7ysvLERcXB0mSBj22ubkZubm5KC0thdls9tMI/S9c3ifA9xoohBBoaWlBVlYWdLrgmJ1299wRyL93b+N7DT2B/D49OW+EXOZFp9MhJyfHo8eYzeaA+0f0hXB5nwDfayAIloyLwtNzR6D+3n2B7zX0BOr7dPe8ERyXREREREQODF6IiIgoqIR18GI0GvHQQw/BaDRqPRSfCpf3CfC9ku+F0++d7zX0hMr7DLmCXSIiIgptYZ15ISIiouDD4IWIiIiCCoMXIiIiCioMXoiIiCioMHghIiKioBK2wctzzz2HgoICmEwmzJ07F9u2bdN6SB7btGkTrrjiCmRlZUGSJLzzzjtO9wsh8OCDDyIzMxNRUVEoLi7G0aNHnY6pr6/HjTfeCLPZjISEBNx6661obW3147sY2urVq3HWWWchLi4OaWlpWLp0KY4cOeJ0TGdnJ+68804kJycjNjYW11xzDaqqqpyOKSkpwWWXXYbo6GikpaXhvvvug9Vq9edbGdLzzz+PadOmqd0v582bh48++ki9P1TeZzAL9nMHzxs9QuXzFJbnDRGGXn/9dWEwGMRLL70kDhw4IG6//XaRkJAgqqqqtB6aRz788EPx85//XPzrX/8SAMTbb7/tdP/jjz8u4uPjxTvvvCP27NkjvvGNb4jCwkLR0dGhHrN48WIxffp08eWXX4r//ve/YsyYMeKGG27w8zsZ3KJFi8TLL78s9u/fL3bv3i2WLFki8vLyRGtrq3rM9773PZGbmyvWrVsntm/fLs4++2xxzjnnqPdbrVYxZcoUUVxcLHbt2iU+/PBDkZKSIlauXKnFWxrQe++9Jz744APx9ddfiyNHjogHHnhAREZGiv379wshQud9BqtQOHfwvMHzRrC+z97CMniZM2eOuPPOO9WfbTabyMrKEqtXr9ZwVCPT9yRkt9tFRkaGeOKJJ9TbGhsbhdFoFH//+9+FEEIcPHhQABBfffWVesxHH30kJEkSZWVlfhu7p6qrqwUA8dlnnwkh5PcVGRkp/vnPf6rHHDp0SAAQW7ZsEULIJ2ydTicqKyvVY55//nlhNptFV1eXf9+AhxITE8WLL74Y8u8zGITauYPnjdD9PIX6eSPspo0sFgt27NiB4uJi9TadTofi4mJs2bJFw5F518mTJ1FZWen0PuPj4zF37lz1fW7ZsgUJCQmYPXu2ekxxcTF0Oh22bt3q9zG7q6mpCQCQlJQEANixYwe6u7ud3uuECROQl5fn9F6nTp2K9PR09ZhFixahubkZBw4c8OPo3Wez2fD666+jra0N8+bNC9n3GSzC4dzB80bwf57C5bwRcrtKD6W2thY2m83pHwkA0tPTcfjwYY1G5X2VlZUA4PJ9KvdVVlYiLS3N6f6IiAgkJSWpxwQau92Ou+++G+eeey6mTJkCQH4fBoMBCQkJTsf2fa+ufhfKfYFk3759mDdvHjo7OxEbG4u3334bkyZNwu7du0PqfQabcDh38LwRvJ+ncDtvhF3wQsHtzjvvxP79+7F582ath+Iz48ePx+7du9HU1IQ333wTN998Mz777DOth0UUtHjeCD1hN22UkpICvV7fr9K6qqoKGRkZGo3K+5T3Mtj7zMjIQHV1tdP9VqsV9fX1Afm7WLFiBd5//31s2LABOTk56u0ZGRmwWCxobGx0Or7ve3X1u1DuCyQGgwFjxoxBUVERVq9ejenTp+OZZ54JufcZbMLh3MHzRvB+nsLtvBF2wYvBYEBRURHWrVun3ma327Fu3TrMmzdPw5F5V2FhITIyMpzeZ3NzM7Zu3aq+z3nz5qGxsRE7duxQj1m/fj3sdjvmzp3r9zEPRAiBFStW4O2338b69etRWFjodH9RUREiIyOd3uuRI0dQUlLi9F737dvndNL95JNPYDabMWnSJP+8kWGy2+3o6uoK+fcZ6MLh3MHzRuh8nkL+vKF1xbAWXn/9dWE0GsUrr7wiDh48KO644w6RkJDgVGkdDFpaWsSuXbvErl27BADx1FNPiV27donTp08LIeQljwkJCeLdd98Ve/fuFVdeeaXLJY8zZ84UW7duFZs3bxZjx44NuCWP3//+90V8fLzYuHGjqKioUL/a29vVY773ve+JvLw8sX79erF9+3Yxb948MW/ePPV+ZSngJZdcInbv3i3Wrl0rUlNTA24p4P333y8+++wzcfLkSbF3715x//33C0mSxMcffyyECJ33GaxC4dzB8wbPG8H6PnsLy+BFCCGeffZZkZeXJwwGg5gzZ4748ssvtR6SxzZs2CAA9Pu6+eabhRDyssdf/OIXIj09XRiNRrFw4UJx5MgRp+eoq6sTN9xwg4iNjRVms1ksX75ctLS0aPBuBubqPQIQL7/8snpMR0eH+MEPfiASExNFdHS0uOqqq0RFRYXT85w6dUpceumlIioqSqSkpIh7771XdHd3+/ndDO6WW24R+fn5wmAwiNTUVLFw4UL1BCRE6LzPYBbs5w6eN15WjwmVz1M4njckIYTwX56HiIiIaGTCruaFiIiIghuDFyIiIgoqDF6IiIgoqDB4ISIioqDC4IWIiIiCCoMXIiIiCioMXoiIiCioMHghIiKioMLghYiIiIIKgxciIiIKKgxeiIiIKKj8f1plx6NqM3nZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### DO NOT CHANGE #####\n",
    "np.random.seed(666)\n",
    "torch.manual_seed(666)\n",
    "\n",
    "train_loader, val_loader, test_loader = get_data(composed=True)\n",
    "model = Composed()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)\n",
    "\n",
    "trainer = ComposedVAE_Trainer(model = model,\n",
    "                          train_loader = train_loader, \n",
    "                          val_loader = val_loader, \n",
    "                          test_loader = test_loader, \n",
    "                          optimizer = optimizer)\n",
    "\n",
    "model = trainer.train(plot=True)\n",
    "plot_reconstr(model, test_loader)\n",
    "trainer.test()\n",
    "\n",
    "##### DO NOT CHANGE #####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fec2302",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "8db44a42376c9e11dd8eaa72232f5203",
     "grade": false,
     "grade_id": "cell-f468068b6849dc8c",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "We will finally produce two ECGs corresponding to the two different classes 'normal' and 'PVC'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "15b4d304",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_24/771126551.py:9: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  c = int(model.classifier(embs[i]).detach().numpy()>0.5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.manual_seed(1)\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)\n",
    "embs = torch.tensor([[0., 0., -3.], [0., 0., 3.]])\n",
    "classes = {0:'normal', 1:'PVC'}\n",
    "model.eval()\n",
    "for i in range(2):\n",
    "        new_data = model.vae.decode(embs[i]).detach().numpy()\n",
    "        axs[i].plot(np.arange(350), new_data)\n",
    "        c = int(model.classifier(embs[i]).detach().numpy()>0.5)\n",
    "        axs[i].set_title(classes[c])\n",
    "fig.show()"
   ]
  }
 ],
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