truss_7x7_zdim4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /home/u0579755/env_dir/lib/python3.6/site-packages (3.0.2)\n",
      "Requirement already satisfied: numpy>=1.10.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (1.16.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (2.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (2.7.5)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (1.0.1)\n",
      "Requirement already satisfied: six in /home/u0579755/env_dir/lib/python3.6/site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /home/u0579755/env_dir/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.8.0)\n",
      "\u001b[33mYou are using pip version 19.0.3, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: scikit-image in /home/u0579755/env_dir/lib/python3.6/site-packages (0.15.0)\n",
      "Requirement already satisfied: imageio>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.5.0)\n",
      "Requirement already satisfied: scipy>=0.17.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.2.1)\n",
      "Requirement already satisfied: networkx>=2.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.3)\n",
      "Requirement already satisfied: PyWavelets>=0.4.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.0.3)\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (3.0.2)\n",
      "Requirement already satisfied: pillow>=4.3.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (5.4.1)\n",
      "Requirement already satisfied: numpy in /home/u0579755/env_dir/lib/python3.6/site-packages (from imageio>=2.0.1->scikit-image) (1.16.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from networkx>=2.0->scikit-image) (4.3.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.7.5)\n",
      "Requirement already satisfied: setuptools in /home/u0579755/env_dir/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image) (40.8.0)\n",
      "Requirement already satisfied: six in /home/u0579755/env_dir/lib/python3.6/site-packages (from cycler>=0.10->matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.12.0)\n",
      "\u001b[33mYou are using pip version 19.0.3, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: scikit-learn in /home/u0579755/env_dir/lib/python3.6/site-packages (0.20.3)\n",
      "Requirement already satisfied: numpy>=1.8.2 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-learn) (1.16.0)\n",
      "Requirement already satisfied: scipy>=0.13.3 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-learn) (1.2.1)\n",
      "\u001b[33mYou are using pip version 19.0.3, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Install\n",
    "!pip3 install matplotlib\n",
    "!pip3 install scikit-image\n",
    "!pip3 install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import\n",
    "import os \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import zipfile \n",
    "from skimage import io \n",
    "from torch.utils.data import Dataset\n",
    "import torch \n",
    "import pandas as pd \n",
    "from torchvision import transforms\n",
    "import copy\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# System Check\n",
    "try:\n",
    "  import google.colab\n",
    "  from google.colab import drive\n",
    "  from google.colab import files\n",
    "  IN_COLAB = True\n",
    "except:\n",
    "  IN_COLAB = False\n",
    "try:\n",
    "    hostname = !hostname\n",
    "    if 'lab' in hostname[0] and '.eng.utah.edu' in hostname[0]:\n",
    "        IN_CADE = True\n",
    "    else:\n",
    "        IN_CADE = False\n",
    "except:\n",
    "    IN_CADE = False\n",
    "\n",
    "assert(not IN_CADE or not IN_COLAB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find GPU\n",
    "def define_gpu_to_use(minimum_memory_mb = 3800):\n",
    "    gpu_to_use = None\n",
    "    try: \n",
    "        os.environ['CUDA_VISIBLE_DEVICES']\n",
    "        print('GPU already assigned before: ' + str(os.environ['CUDA_VISIBLE_DEVICES']))\n",
    "        return\n",
    "    except:\n",
    "        pass\n",
    "    torch.cuda.empty_cache()\n",
    "    for i in range(16):\n",
    "        free_memory = !nvidia-smi --query-gpu=memory.free -i $i --format=csv,nounits,noheader\n",
    "        if free_memory[0] == 'No devices were found':\n",
    "            break\n",
    "        free_memory = int(free_memory[0])\n",
    "        if free_memory>minimum_memory_mb-500:\n",
    "            gpu_to_use = i\n",
    "            break\n",
    "    if gpu_to_use is None:\n",
    "        print('Could not find any GPU available with the required free memory of ' +str(minimum_memory_mb) + 'MB. Please use a different system for this assignment.')\n",
    "    else:\n",
    "        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_to_use)\n",
    "        print('Chosen GPU: ' + str(gpu_to_use))\n",
    "        x = torch.rand((256,1024,minimum_memory_mb-500)).cuda()\n",
    "        x = torch.rand((1,1)).cuda()\n",
    "        del x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen GPU: 0\n"
     ]
    }
   ],
   "source": [
    "# Reserve GPU\n",
    "if IN_CADE:\n",
    "    define_gpu_to_use()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Visualize Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Opens zip file and places data in datsets folder\n",
    "def open_zip(filename):\n",
    "    zip_ref = zipfile.ZipFile(filename, 'r')\n",
    "    zip_ref.extractall('datasets')\n",
    "    zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return indexes to split data by for train, val, and test\n",
    "# Splits are 70% train, 10% val, 20% test\n",
    "def get_split(array_to_split, split):\n",
    "    np.random.seed(0)\n",
    "    np.random.shuffle(array_to_split)\n",
    "    np.random.seed()\n",
    "    if split == 'train':\n",
    "        array_to_split = array_to_split[:int(len(array_to_split)*0.7)]\n",
    "    elif split == 'val':\n",
    "        array_to_split = array_to_split[int(len(array_to_split)*0.7):int(len(array_to_split)*0.8)]\n",
    "    elif split == 'test':\n",
    "        array_to_split = array_to_split[int(len(array_to_split)*0.8):]\n",
    "    return array_to_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset class for Truss (Bracket/Beam) dataset\n",
    "class TrussDataset(Dataset):\n",
    "    def __init__(self, csv_file, root_dir, split='train'):\n",
    "        self.root_dir = root_dir\n",
    "        self.split = split\n",
    "        # Get the split\n",
    "        all_attributes_frame = pd.read_csv(root_dir + csv_file)\n",
    "        ids = get_split(pd.unique(all_attributes_frame['ID']), split)\n",
    "        self.attributes_frame = all_attributes_frame[all_attributes_frame['ID'].isin(ids)]\n",
    "              \n",
    "    def __len__(self):\n",
    "        return len(self.attributes_frame)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        image_name = os.path.join(self.root_dir, self.attributes_frame.iloc[idx, 1])\n",
    "        image = io.imread(image_name)\n",
    "        image = image.astype(np.float32)\n",
    "        image = image[:,:,:3]\n",
    "        image = image/255\n",
    "        image = np.swapaxes(image, 0, 1)\n",
    "        image = np.swapaxes(image, 0, 2)\n",
    "        attributes = self.attributes_frame.iloc[idx, 2:].values\n",
    "        attributes = attributes.astype('float')\n",
    "        attribute_names = self.attributes_frame.columns[2:].values\n",
    "        return torch.FloatTensor(image), torch.FloatTensor(attributes), list(attribute_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open zip of dataset\n",
    "open_zip('trussData.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train, val, and test datasets\n",
    "train_dataset = TrussDataset('truss_data.csv', 'datasets/trussData/')\n",
    "val_dataset = TrussDataset('truss_data.csv', 'datasets/trussData/', split='val')\n",
    "test_dataset = TrussDataset('truss_data.csv', 'datasets/trussData/', split='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualing a few cases of the training set\n",
    "for batch_idx, (image, attribute, name) in enumerate(train_dataset):\n",
    "    if batch_idx%250 == 0: \n",
    "        plt.figure()\n",
    "        plt.title(str(name) + ' ' + str(attribute.cpu().numpy()))\n",
    "        plt.imshow(image[:,:,:].permute([1, 2, 0]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculates L2 for images and MSE for regression - want both close to 0\n",
    "def get_score_model(model, data_loader):\n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        distances = []\n",
    "        mse = []\n",
    "        for image, attribute, name in data_loader:\n",
    "            image = image.cuda()\n",
    "            recon, mu, logvar, reg = model(image)\n",
    "            # Reconstruction Score\n",
    "            euclidean_dist = torch.pow(torch.sum(torch.pow((image - recon), 2)), 0.5)\n",
    "            distances.append(euclidean_dist.cpu().numpy())\n",
    "            # Regression Score\n",
    "            y_true = attribute.cpu().numpy()\n",
    "            y_pred = reg.cpu().numpy()         \n",
    "            mse.append(mean_squared_error(y_true, y_pred))\n",
    "    \n",
    "    return np.mean(distances), np.mean(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classes to create VAE, ability to change kernel_size of filters for convolution layers\n",
    "# and the latent representation dim\n",
    "class ConvBlock(torch.nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size):\n",
    "        super(ConvBlock, self).__init__()\n",
    "        padding = int((kernel_size - 1) / 2)\n",
    "        self.conv = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=padding)\n",
    "        self.batch = torch.nn.BatchNorm2d(num_features=out_channels)\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.batch(x)\n",
    "        x = self.relu(x)\n",
    "        return x\n",
    "\n",
    "class TransBlock(torch.nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(TransBlock, self).__init__()\n",
    "        self.convT = torch.nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=2, stride=2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.convT(x)\n",
    "        return x\n",
    "\n",
    "class Flatten(torch.nn.Module):\n",
    "    def forward(self, x):\n",
    "        return x.view(x.size(0), -1)\n",
    "\n",
    "class UnFlatten(torch.nn.Module):\n",
    "    def forward(self, x):\n",
    "        return x.view(-1, 32, 5, 10) # see if there is a way to not have this hardcoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VAE with Regression - The reconstruction and regression are done from the\n",
    "# latent variables calculated from the reparametrization (distribution)\n",
    "class VAE_REG(torch.nn.Module):\n",
    "    def __init__(self, kernel_size, z_dim):\n",
    "        super(VAE_REG, self).__init__()\n",
    "        self.encoder = torch.nn.Sequential(\n",
    "        ConvBlock(in_channels=3, out_channels=8, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=8, out_channels=8, kernel_size=kernel_size),\n",
    "        torch.nn.MaxPool2d(kernel_size=2),\n",
    "        ConvBlock(in_channels=8, out_channels=16, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=16, out_channels=16, kernel_size=kernel_size),\n",
    "        torch.nn.MaxPool2d(kernel_size=2),\n",
    "        ConvBlock(in_channels=16, out_channels=32, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=32, out_channels=32, kernel_size=kernel_size),\n",
    "        torch.nn.MaxPool2d(kernel_size=2),\n",
    "        Flatten()) \n",
    "        \n",
    "        self.mu = torch.nn.Linear(1600, z_dim)\n",
    "        self.logvar = torch.nn.Linear(1600, z_dim)\n",
    "        self.fc = torch.nn.Linear(z_dim, 1600)\n",
    "        \n",
    "        self.decoder = torch.nn.Sequential(\n",
    "        UnFlatten(),\n",
    "        TransBlock(in_channels=32, out_channels=32),\n",
    "        ConvBlock(in_channels=32, out_channels=32, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=32, out_channels=16, kernel_size=kernel_size),\n",
    "        TransBlock(in_channels=16, out_channels=16),\n",
    "        ConvBlock(in_channels=16, out_channels=16, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=16, out_channels=8, kernel_size=kernel_size),\n",
    "        TransBlock(in_channels=8, out_channels=8),\n",
    "        ConvBlock(in_channels=8, out_channels=8, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=8, out_channels=3, kernel_size=kernel_size))\n",
    "        \n",
    "        self.regression = torch.nn.Sequential(\n",
    "            torch.nn.Linear(in_features=z_dim, out_features=4),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(in_features=4, out_features=1))\n",
    "            \n",
    "    def reparametrize(self, mu, logvar):\n",
    "        std = logvar.mul(0.5).exp_()\n",
    "        esp = torch.cuda.FloatTensor(mu.size()).normal_()\n",
    "        z = mu + std * esp\n",
    "        return z\n",
    "    \n",
    "    def bottleneck(self, x):\n",
    "        mu, logvar = self.mu(x), self.logvar(x)\n",
    "        z = self.reparametrize(mu, logvar)\n",
    "        return z, mu, logvar\n",
    "    \n",
    "    def representation(self, x):\n",
    "        return self.bottleneck(self.encoder(x))[0]\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        z, mu, logvar = self.bottleneck(x) # z is latent representation\n",
    "        x = self.fc(z)\n",
    "        x = torch.sigmoid(self.decoder(x)) # VAE output\n",
    "        r = self.regression(z) # Regression output\n",
    "        return x, mu, logvar, r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function for this task, combines reconstruction and KL for the image task and the MSELoss\n",
    "# for the regression task\n",
    "def loss_function(reconstruction, image, mu, logvar, attributes, r, batch_size):\n",
    "    reconstruction_loss = torch.nn.functional.binary_cross_entropy(reconstruction, image)\n",
    "    kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "    kl_loss /=  batch_size * image.shape[1] * image.shape[2] * image.shape[3]\n",
    "    reg_loss =  torch.nn.MSELoss()\n",
    "    return reconstruction_loss, kl_loss, reg_loss(attributes, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Starting Training!\n",
      "\n",
      "Epoch: 0\n",
      "Total Loss: 0.713194910969053\n",
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      "Validation Reconstruction Score: 42.42407\n",
      "Validation MSE Score: 0.0048204134\n",
      "\n",
      "Epoch: 1\n",
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      "Trainig Reconstruction Score: 39.607414\n",
      "Training MSE Score: 0.005911796\n",
      "Validation Reconstruction Score: 39.604763\n",
      "Validation MSE Score: 0.0055995886\n",
      "\n",
      "Epoch: 2\n",
      "Total Loss: 0.5607588112354278\n",
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      "Trainig Reconstruction Score: 38.31076\n",
      "Training MSE Score: 0.0038769932\n",
      "Validation Reconstruction Score: 38.266376\n",
      "Validation MSE Score: 0.003475677\n",
      "\n",
      "Epoch: 3\n",
      "Total Loss: 0.5282421163150243\n",
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      "Validation Reconstruction Score: 37.15336\n",
      "Validation MSE Score: 0.0037216132\n",
      "\n",
      "Epoch: 4\n",
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      "Validation Reconstruction Score: 36.68665\n",
      "Validation MSE Score: 0.0035651186\n",
      "\n",
      "Epoch: 5\n",
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      "Validation Reconstruction Score: 35.887245\n",
      "Validation MSE Score: 0.003846015\n",
      "\n",
      "Epoch: 6\n",
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      "Validation Reconstruction Score: 35.53616\n",
      "Validation MSE Score: 0.0036130743\n",
      "\n",
      "Epoch: 7\n",
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      "Training MSE Score: 0.003159517\n",
      "Validation Reconstruction Score: 35.19821\n",
      "Validation MSE Score: 0.0036010817\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 8\n",
      "Total Loss: 0.4660959086247853\n",
      "Reconstruction Loss: 0.46092789130551476\n",
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      "Training MSE Score: 0.0030916592\n",
      "Validation Reconstruction Score: 35.58988\n",
      "Validation MSE Score: 0.0032321739\n",
      "\n",
      "Epoch: 9\n",
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      "Validation Reconstruction Score: 34.9232\n",
      "Validation MSE Score: 0.00329964\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 10\n",
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      "Validation Reconstruction Score: 35.04667\n",
      "Validation MSE Score: 0.003440521\n",
      "\n",
      "Epoch: 11\n",
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      "Validation Reconstruction Score: 34.69972\n",
      "Validation MSE Score: 0.0032386056\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 12\n",
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      "Reconstruction Loss: 0.44544331388814107\n",
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      "Validation Reconstruction Score: 34.654972\n",
      "Validation MSE Score: 0.003697612\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 13\n",
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      "Validation Reconstruction Score: 34.70441\n",
      "Validation MSE Score: 0.0034160388\n",
      "\n",
      "Epoch: 14\n",
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      "Validation Reconstruction Score: 34.597984\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 15\n",
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      "Validation Reconstruction Score: 34.65378\n",
      "Validation MSE Score: 0.003244981\n",
      "\n",
      "Epoch: 16\n",
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      "Validation Reconstruction Score: 34.324547\n",
      "Validation MSE Score: 0.0032100028\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 17\n",
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      "\n",
      "Epoch: 18\n",
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      "Validation Reconstruction Score: 34.278763\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 19\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 20\n",
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      "\n",
      "Epoch: 21\n",
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      "Validation Reconstruction Score: 34.156136\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 22\n",
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      "Validation Reconstruction Score: 34.40296\n",
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      "\n",
      "Epoch: 23\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 24\n",
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      "\n",
      "Epoch: 25\n",
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      "\n",
      "Epoch: 26\n",
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     "text": [
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      "Training MSE Score: 0.0029262938\n",
      "Validation Reconstruction Score: 34.076744\n",
      "Validation MSE Score: 0.003119769\n",
      "\n",
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      "Validation Reconstruction Score: 34.02932\n",
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      "\n",
      "Epoch: 28\n",
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      "\n",
      "Epoch: 29\n",
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      "Validation Reconstruction Score: 33.941193\n",
      "Validation MSE Score: 0.0031062204\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 30\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 33\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Epoch: 38\n",
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      "\n",
      "Epoch: 39\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 41\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Epoch: 48\n",
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      "Validation Reconstruction Score: 33.7213\n",
      "Validation MSE Score: 0.0031064472\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 49\n",
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      "\n",
      "Epoch: 50\n",
      "Total Loss: 0.41619780148778646\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 58\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     "text": [
      "Validation Reconstruction Score: 33.652626\n",
      "Validation MSE Score: 0.0032134613\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Validation MSE Score: 0.0032672354\n",
      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     "text": [
      "Validation Reconstruction Score: 33.627686\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "KL Loss: 0.001976036699488759\n",
      "Regression Loss: 0.0015296001494529525\n",
      "Trainig Reconstruction Score: 32.846905\n",
      "Training MSE Score: 0.0015032145\n",
      "Validation Reconstruction Score: 33.435936\n",
      "Validation MSE Score: 0.0035035396\n",
      "\n",
      "Epoch: 194\n",
      "Total Loss: 0.39999287383896964\n",
      "Reconstruction Loss: 0.39646277129650115\n",
      "KL Loss: 0.00197489321165319\n",
      "Regression Loss: 0.0015552072261925786\n",
      "Trainig Reconstruction Score: 32.84631\n",
      "Training MSE Score: 0.0014824723\n",
      "Validation Reconstruction Score: 33.445057\n",
      "Validation MSE Score: 0.0035241686\n",
      "\n",
      "Epoch: 195\n",
      "Total Loss: 0.3999920044626508\n",
      "Reconstruction Loss: 0.3964878158909934\n",
      "KL Loss: 0.0019770795329740005\n",
      "Regression Loss: 0.0015271063070810798\n",
      "Trainig Reconstruction Score: 32.84334\n",
      "Training MSE Score: 0.0014969647\n",
      "Validation Reconstruction Score: 33.432026\n",
      "Validation MSE Score: 0.0034933104\n",
      "\n",
      "Epoch: 196\n",
      "Total Loss: 0.3999596106154578\n",
      "Reconstruction Loss: 0.39645566812583377\n",
      "KL Loss: 0.001974462832523776\n",
      "Regression Loss: 0.0015294789153683399\n",
      "Trainig Reconstruction Score: 32.848442\n",
      "Training MSE Score: 0.0014992929\n",
      "Validation Reconstruction Score: 33.455666\n",
      "Validation MSE Score: 0.0034851942\n",
      "\n",
      "Epoch: 197\n",
      "Total Loss: 0.39989247364657265\n",
      "Reconstruction Loss: 0.3964023381471634\n",
      "KL Loss: 0.0019734087749384344\n",
      "Regression Loss: 0.001516724777424575\n",
      "Trainig Reconstruction Score: 32.843006\n",
      "Training MSE Score: 0.0014869001\n",
      "Validation Reconstruction Score: 33.443214\n",
      "Validation MSE Score: 0.003468641\n",
      "\n",
      "Epoch: 198\n",
      "Total Loss: 0.39994958170822686\n",
      "Reconstruction Loss: 0.396463925072125\n",
      "KL Loss: 0.0019728289135465663\n",
      "Regression Loss: 0.0015128293357390377\n",
      "Trainig Reconstruction Score: 32.84221\n",
      "Training MSE Score: 0.0014799704\n",
      "Validation Reconstruction Score: 33.439064\n",
      "Validation MSE Score: 0.0034304685\n",
      "\n",
      "Epoch: 199\n",
      "Total Loss: 0.3999645684446607\n",
      "Reconstruction Loss: 0.3964925953320095\n",
      "KL Loss: 0.001972894952632487\n",
      "Regression Loss: 0.0014990767254078362\n",
      "Trainig Reconstruction Score: 32.84362\n",
      "Training MSE Score: 0.0014649066\n",
      "Validation Reconstruction Score: 33.43554\n",
      "Validation MSE Score: 0.003517461\n"
     ]
    }
   ],
   "source": [
    "# VAE conv kernel size = 7 z_dim=2\n",
    "BATCH_SIZE = 10\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)\n",
    "train_loader_score = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=0)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)\n",
    "\n",
    "#instantiate your model here:\n",
    "model = VAE_REG(kernel_size=7, z_dim=4)\n",
    "model = model.cuda()\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, nesterov=True)\n",
    "n_epochs = 200\n",
    "# Learning rate is reduced after plateauing to stabilize the end of training.\n",
    "# use the learning rate scheduler as defined here. Example on how to integrate it to training in\n",
    "# https://pytorch.org/docs/stable/optim.html#torch.optim.lr_scheduler.StepLR\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=150, gamma=0.1)\n",
    "\n",
    "train_recon_scores = []\n",
    "train_r2_scores = []\n",
    "val_recon_scores = []\n",
    "val_r2_scores = []\n",
    "target_score = 35.5 # Want score to be less than this\n",
    "best_score = 50\n",
    "best_model = copy.deepcopy(model)\n",
    "\n",
    "print('Starting Training!')\n",
    "for epoch in range(n_epochs):\n",
    "    print('\\nEpoch: ' + str(epoch))\n",
    "    scheduler.step()\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    total_losses = []\n",
    "    reconstruction_losses = []\n",
    "    kl_losses = []\n",
    "    reg_losses = []\n",
    "    for image, attribute, name in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        image = image.cuda()\n",
    "        attribute = attribute.cuda()\n",
    "        recon, mu, logvar, reg = model(image)\n",
    "        reconstruction_loss, kl_loss, reg_loss = loss_function(recon, image, mu, logvar, attribute, reg, BATCH_SIZE)\n",
    "        loss = reconstruction_loss + kl_loss + reg_loss\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        total_losses.append(loss.item())\n",
    "        reconstruction_losses.append(reconstruction_loss.item())\n",
    "        kl_losses.append(kl_loss.item())\n",
    "        reg_losses.append(reg_loss.item())\n",
    "        \n",
    "    print('Total Loss: ' + str(np.mean(total_losses)))\n",
    "    print('Reconstruction Loss: ' + str(np.mean(reconstruction_losses)))\n",
    "    print('KL Loss: ' + str(np.mean(kl_losses)))\n",
    "    print('Regression Loss: ' + str(np.mean(reg_losses)))\n",
    "    \n",
    "    # Get training data accuracy\n",
    "    train_recon_score, train_r2 = get_score_model(model, train_loader_score)\n",
    "    print('Trainig Reconstruction Score: ' + str(train_recon_score))\n",
    "    print('Training MSE Score: ' + str(train_r2))\n",
    "    train_recon_scores.append(train_recon_score)\n",
    "    train_r2_scores.append(train_r2)\n",
    "    \n",
    "    # Get validation data accuracy\n",
    "    val_recon_score, val_r2 = get_score_model(model, val_loader)\n",
    "    print('Validation Reconstruction Score: ' + str(val_recon_score))\n",
    "    print('Validation MSE Score: ' + str(val_r2))\n",
    "    val_recon_scores.append(val_recon_score)\n",
    "    val_r2_scores.append(val_r2)\n",
    "    \n",
    "    # Save Best Model - Based on Reconstruction Score\n",
    "    if val_recon_score < target_score and val_recon_score < best_score:\n",
    "        best_score = val_recon_score\n",
    "        best_model = copy.deepcopy(model)\n",
    "        print('New Best Model Saved!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training and Validation Score\n",
    "plt.plot(range(n_epochs), train_recon_scores, 'b', range(n_epochs), val_recon_scores, 'r')\n",
    "plt.title('L2 Distance for VAE')\n",
    "plt.legend(['Training Data', 'Validation Data'])\n",
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel('L2 Distance Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training and Validation Score\n",
    "plt.plot(range(n_epochs), train_r2_scores, 'b', range(n_epochs), val_r2_scores, 'r')\n",
    "plt.title('MSE for VAE')\n",
    "plt.legend(['Training Data', 'Validation Data'])\n",
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel('MSE Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RyUBFRKSk4k4SfQeAvWZ2Vy4n8yeJToorr7wqsqy1tdWJ6+pqI9uk8mvqhw5G70PfsNEdnGjVytVOvPOj7U5cU+uec8r4U5x4WEt0oLLhLcOduKlpsBP79w5nGtgsnUw18yNenK5e7tdb/TwyDqgWTarP7YNNMvyuRBYUvhYda0C1hEyQHYc/cN4+bxrMF154wYmXLl2KSlTsSaJFRKTC5FND/zrJJWFJZmhvG2mSaBGR0ojboN8DYDKAGQDaAPxzbxtqkmgRkdKIdR+6mW3t+ZrkjwA8U7CM+qHHHns0smzKKa1OfOlFn3fixia3fu3XpgelmRBg/Pjx3j5uXXPF++72a1Ysd+LtXg2+pqE5co5x40Y68dRTTnPiESPcGntDg/tHuqoq+iMVmag7Um327t82t+7p19SB3Gu60fFi/Jp7dB9/WSEmDMm5nl2A0nXG/oQY/Qf9pabuP8PQ2OhOIn/55Zc78XmzznPie35wT3ESK5JYV+gkU5+s+SKAyuxJEBE5hmS8Qg8niZ4NYATJjQC+A2A2yRkIbhZYB+CGIuYoIiJZiDtJ9L1FyEVERPJwzI3lUiqrl69z4n9b/m9OPOeKK514wqSJTuyPYwEAAwa4Y8y0DHfHbmlv+ciJl65yB67oPODe645D0fFidnW4d6h2HnT3OeEUdzz5Uce3OPHAgW6OADBgoNsfUFvtfW9+rTpyb3u0xuuV2XHwwEEn3rNvr7feva9/sDdZuN8XAETfg6qq4o/VEmF+mH9/QiFkGvud9J81yHzMcnwf/jmPO94d/+jmm2+O7PP9e9zf5X17Dka2KRc9+i8ikhBq0EVEEkINuohIQqhBFxFJCHWKxuD36XV3pd+uL4/89DEnnjHd7Wycdf6FkX2avIci6pvcjrzWCa1O/Il9Zzjx6hXuYF6siU5wUVvrfnN7uvY58f49bqdpe43bqVQ/KDrgV2OT+wIN8CbWGOBPZl3td0ZGrzvM63Q75PWSbvnIzXNXW5sT11W5D3KNaZ0cOceI4e6IFoPqB3pb5DYgWByl6Cj0zxFvMK9MnaRp9sjxPJGHm9I9dVXgl2vQoOgDft+88dtOvGjxYid++un5hU0iB7pCFxFJCDXoIiIJoQZdRCQhVEOPobsIE80uWryszxgArrjqCiceM3KMEzc2NjjxzJnuePiTW09w4l373fo4AGxv2+rE3Z3uQxN7vYcoDne7ter6gQcix7Qqd0CvKriTZtTVujV1q448TRPR1dnpxFu3unmveHeJE7cfdB+66trh7r92y4eRc5zU6vZrnHjiFCeub3Drq9VpHgbLVbSe7a/P+xQRlTLwVs419XQ/GLmOfRbje62qdq+DZ86c4cSDh7o/3w/Peyjnc8SlK3QRkYRQgy4ikhDZTBI9juTLJJeRfI/kjeHyYSQXkFwVfu511iIRESm+bCaJHgVglJm9Q7IJwNsALgNwLYCdZnYnyVsBDDWzW/o6VlIniS6XT8/+lBOfdtp0J/YHyvInijh0KDqo0K6O3U68s92tke8/0OHE+/a7938Pb4zehz5i5Ch3m+FDvDy9+7u9PPfs3hM55rvvvuPEv1/4RmQbl18rzf1e64ZGN8+ZM8924qlTT3Lixnq3TwMA6urc/gK/7p7rZNfZbtO3vu8hL4RCTFRdjPP4r10h+g/8HDZs2OTE99+f+2C1hZwkus3M3gm/3gNgOYAxAL4A4MFwswcRNPIiIlImOdXQSbYCmAlgIYDjzazn8bstAI7vZR9NEi0iUgJZN+gkGwH8DMBNZub8323B/xhp/9fRJNEiIqWR1Y2zJGsRNOYPm9nPw8VbSY4ys7awzr6tWElKei//5hUnHjZ0mBNPnOyOT1LnTZBRW1sbOWa9V/cd3OTWu/fsdmvs+7172SP1cACNTe55q7wJmvd6NfLVa9c48fMvPB85Zu7yr+Hu2+v2Obz6u1f6jCePa40cY9pMd9LtMWPcZwnqG9w+iLo69z1KN65NrnXg6Pr+WzP35VoDL8X4O+PGue/xf772usg+9z1QmEngsrnLhQimnFtuZv+Ssmo+gGvCr68B8FRBMhIRkViyuUI/F8BfAfgjyUXhsr8BcCeAn5K8DsB6AFf0sr+IiJRANpNEv4re/yeLjvEqIiJlobFcEmTlGne887GtE5x4ANxadjZjjzR72zQ2uDX2Lm9gmyNpBrrpPHzYiTduWO/Ev3/tVXe9N55Mf7Vmw7qMy8aOce/RnzDJfc/GjXXjpiHNkWMO9u799/sxqopwr7Uvi+dZsjmKv1feeRTle/XiTGfwcxg9dlRkm1nnnOvEr77+WozM9Oi/iEhiqEEXEUkINegiIgmhBl1EJCHUKZogU6a4E1jU1bgPpZC5D/xUXe1Opuw/2FLtdYIeOBgd8GvdOvdBoWd+WYgHhZJh46a2PmPAHXSsNs1v7IWf+ZwTnzhlkhM3NLhPaNfU+A8rVcbk1tF+1dw7OEsyqXae+/u/UwBw3qfOd+JabxKNuVkeW1foIiIJoQZdRCQh1KCLiCSEauj92Jwrr3LisePHOXFdnTcoVgFqpZEjeHXPjjSTUbyz8K28zyuBzq7osueffdqJt0073YlbT3T7ViaMH+/EjU2NTpxuALBMClG7Ltfk1JWg1huE7cxzPhnrOLpCFxFJCDXoIiIJkc8k0XeQ3ERyUfhxafHTFRGR3mRTQ+8C8K3USaJJLgjX3W1mdxUvvWPX5y77YmTZtFNOduIab+Cs3GuQ6bbve5Cl7iPupNC7OnY58ZuL3o7ss3nHjhzzkny8s3RJn7H/S3/V1V924jFpBo/yJ7cuxuTKxzL/9RvgTUaTrWyGz20D0BZ+vYdkzyTRIiJSQfKZJBoAvk5yCcn7SA7tZR9NEi0iUgL5TBJ9D4DJAGYguIL/53T7aZJoEZHSiD1JtJltTVn/IwDPFCXDhPqTWec58fmz3AHu/XvIASBTmTL3iXmj2x/xauRd3o3Pm7duceLXXv6dE69Z747bkg3/quJI2q2kUPxb2R96eJ4Tjxs/NrLP2We7P5+trd4zD7Xe5CnVmccNUt29d4z5zEjsSaJJpvacfBHA0lgZiIhIQeQzSfQckjMQXOatA3BDUTIUEZGs5DNJ9LOFT0dEROLSWC5FMmGUO7nvFVf/hRPXe5Mtl4J59fGDh6Jjl2/e5NbIFy1a5MTvLSt8ZU0188qy4cONaZY91uc+p/vjx7ROdGJ/nCEAaGpyfwf8e93jjCmTFIw56vqx+4qJiCSMGnQRkYRQgy4ikhBq0EVEEkKdogXw1a9eF1k2cuRoJy5EB4//4FCmB4m6vQmc29t3OvETP388sk/79vaY2cmxbIk3AJgfp9PSMtyJzzjzbCeeepI7McegercTNd1ky8f6oGG6QhcRSQg16CIiCaEGXUQkIVRDz8JYb7Ciq+dc7cQDBw4sSR5+xdyvoR8+dNiJ16xe5cQ/e/LJYqQlEst2r7/mheee9WJ3++mnnOjE46ZMiRxz7Cj3AabBQ5uduK7WnYy5GA8vFaJufyTngfYCukIXEUkINegiIgmRzfC5A0m+SXJxOEn03HD5RJILSa4m+RjJukzHEhGR4smmhn4IwAVmtjec6OJVks8B+CaCSaIfJfkDANchmMWo3/vGN77hxE1NTU5cjHtbs7nH3L+v/OChQ0689F333t9fvbQAIkmxePnKPuN0pk5172UfN3ayG3uDhjU0+Pe6R695q726e41Xlyfd9dFjpPvddoeo29PREdkmGxmv0C2wNwxrww8DcAGAJ8LlDwK4LFYGIiJSEFnV0ElWh5NbbAOwAMAaALvMrGc2q40AxvSyryaJFhEpgawadDPrNrMZAMYCOAvAydmeQJNEi4iURk73oZvZLpIvAzgHQDPJmvAqfSyATcVIsNBuvOnGyLIhg4c4cTlq5Ic73XvIOw93Ro6xf+8BJ35/rVtD/M1LL+aTokjiLFu2qs84k8bBwyPLRo8a4cRjx7jFiaEjhjnx4EFuXX7b5q2RY37Q9oETb928Pac8e2Rzl0sLyebw60EALgawHMDLAL4UbnYNgKdiZSAiIgWRzRX6KAAPkqxG8Afgp2b2DMllAB4l+V0A7wK4t4h5iohIBtlMEr0EwMw0y9ciqKeLiEgF6PdjuQwe7N4j/td/fb0TNza69atSjY98xJuQed++fU68dq1by3v73cVOvHv37sgxO3ZFl4lI8eztiM4PsNJbtnLFilKlk5Ee/RcRSQg16CIiCaEGXUQkIdSgi4gkRL/rFL3hhhuceOTIkWXK5KgjR6KD7XR0uB2Y99//Y2/9R0XNSUSOPbpCFxFJCDXoIiIJoQZdRCQhKqqGPmnK5Miyv7j8z5144KBBpUqnV93eQ0M7tkQH27n3wXlO3Hn4YFFzEhHRFbqISEKoQRcRSYh8Jol+gOQHJBeFHzOKn66IiPQmn0miAeDbZvZEH/s6hgxpxn+89M8+jk88YZKzvmlIc2SfUg2mlcq/r/ywNxnzH5ctc+Jnn3m66DmJiGSSzfC5BiDdJNEiIlJBYk0SbWYLw1V/T3IJybtJDuhl348nid61S09HiogUS6xJoklOA3AbgsmiPwFgGIBbetn340mim5uHFihtERHxxZ0k+hIzuytcfIjk/QD+e6b96+sHYfqM0z+Oa2trczl9QfgTTwDAnr0dTvz6b19z4oVvv1XUnERECiHuJNHvkxwVLiOAywAsLWaiIiLSt3wmiX6JZAsAAlgE4L8UMU8REckgn0miLyhKRiIiEktJx3IhWfS6eXCX5VGdnV1OvOnDDyP7zHv4oaLmJCJSCnr0X0QkIdSgi4gkhBp0EZGEUIMuIpIQFTXBRRydXW6n5872did+6pfPOnHbhminqIhIEugKXUQkIdSgi4gkhBp0EZGEKHkNPfXBn2wmr+ju7nbiQ4fcyZZXrVztxM8/95wTHzzsTk4hIpJUukIXEUkINegiIgmhBl1EJCHoD2ZV1JOR2wGsBzACwI6SnTg+5VlY/SHP/pAjoDwLrdLznGBmLZk2KmmD/vFJybfM7MySnzhHyrOw+kOe/SFHQHkWWn/JMxOVXEREEkINuohIQpSrQf9hmc6bK+VZWP0hz/6QI6A8C62/5NmnstTQRUSk8FRyERFJCDXoIiIJUfIGneQlJFeQXE3y1lKfvzck7yO5jeTSlGXDSC4guSr8PLTMOY4j+TLJZSTfI3ljheY5kOSbJBeHec4Nl08kuTB87x8jWVfOPHuQrCb5Lslnwrji8iS5juQfSS4i+Va4rKLe9zCnZpJPkHyf5HKS51RSniRPCl/Dno8OkjdVUo75KGmDTrIawPcBfBbAVABzSE4tZQ59eADAJd6yWwG8aGYnAHgxjMupC8C3zGwqgE8C+Fr4+lVanocAXGBm0wHMAHAJyU8C+EcAd5vZFAAfAbiujDmmuhHA8pS4UvP8tJnNSLlfutLedwD4VwDPm9nJAKYjeF0rJk8zWxG+hjMA/AcA+wE8WUk55sXMSvYB4BwAL6TEtwG4rZQ5ZMivFcDSlHgFgFHh16MArCh3jl6+TwG4uJLzBFAP4B0AZyN4Eq8m3c9CGfMbi+AX+AIAzwBghea5DsAIb1lFve8AhgD4AOHNFpWaZ0penwHwWiXnmOtHqUsuYwBsSIk3hssq1fFm1hZ+vQXA8eVMJhXJVgAzASxEBeYZljEWAdgGYAGANQB2mVnPnIGV8t5/D8DNAI6E8XBUZp4G4Fck3yZ5fbis0t73iQC2A7g/LGH9O8kGVF6ePa4C8Ej4daXmmBN1imbJgj/dFXGPJ8lGAD8DcJOZdaSuq5Q8zazbgn9rxwI4C8DJZU4pguSfAdhmZm+XO5cszDKzMxCUK79G8vzUlRXyvtcAOAPAPWY2E8A+eKWLCskTYb/I5wE87q+rlBzjKHWDvgnAuJR4bLisUm0lOQoAws/bypwPSNYiaMwfNrOfh4srLs8eZrYLwMsIShfNJHsmVamE9/5cAJ8nuQ7AowjKLv+KyssTZrYp/LwNQc33LFTe+74RwEYzWxjGTyBo4CstTyD4w/iOmW0N40rMMWelbtD/AOCE8C6COgT/8swvcQ65mA/gmvDraxDUrMuGwRRP9wJYbmb/krKq0vJsIdkcfj0IQZ1/OYKG/UvjtowQAAAA80lEQVThZmXP08xuM7OxZtaK4GfxJTO7GhWWJ8kGkk09XyOo/S5Fhb3vZrYFwAaSJ4WLLgSwDBWWZ2gOjpZbgMrMMXdl6Ii4FMBKBDXVvy13J0JKXo8AaAPQieBK4zoE9dQXAawC8GsAw8qc4ywE/wouAbAo/Li0AvM8HcC7YZ5LAfxduHwSgDcBrEbwr+6Acr/vKTnPBvBMJeYZ5rM4/Hiv5/em0t73MKcZAN4K3/tfABhaaXkCaADQDmBIyrKKyjHuhx79FxFJCHWKiogkhBp0EZGEUIMuIpIQatBFRBJCDbqISEKoQRcRSQg16CIiCfH/AZMwZUQbsHMlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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66KLIPvv06dNiXb61a9c6OV3PccGCBU4+cuSIk7t27erkfv36Odnv61911VWRY/h99kxfX5FsvP7665Ft99zjDp3r17tLTDQ0NBRtkehaM1sZ3j4MYBOAgQBuADAjfNgMBIO8iIiUScb50FOFy81dAmApgP5m1vRybQ+A/s085y4Ad+VfooiIZCPrk6IkewB4FsBXzexQ6n0W9G3StlO0SLSISGlku0h0JwBzAcwzs/8It20BMMbMasM++2Izi66Y4O5HPfQC+Ndv+/OVTJ482ckTJ050ckVFRc7H3LZtm5P962P9hTkyXRufTl1dnZO/8Y1vOPn555+PPGf//v1OPnXqVIvH8Be39uec6dWrV+Q5AwcOdLL/XoFrrrnGyf78O37fXqQ5/nstvvWtbzl53bp1xemhM/hJmAZgU9NgHnoRwO3h7dsBvJBpXyIi0nqy6aFfBeBzANaRXB1u+zqA7wF4huRUALsA3NI6JYqISDayWST6VwDYzN3jmtkuIiIlprlc2pAOHTo42V+I+oYbbnDylClTnFxdXZ3zMf3/H34vOh/+te733nuvk2fOnOnkvXv3RvaRqWdeDP4c6v687X6P/LzzznPyxz/+8cg+/T77qFGjCilREmr69OlOnjp1quZyERFpTzSgi4gkhAZ0EZGE0IAuIpIQOikaU9kstuwv4Oy/sejqq6928mmnnVac4grkT7b12GOPObmmpsbJpTgBmo983qx09tlnO/mSSy5x8ic/+Ukn+28O69mzZ851SttHUidFRUTaEw3oIiIJoQFdRCQh1EMvE/9NK/6kVsOGDYs85/rrr3fyHXfc4WR/Mqm4+OEPf9hi3rFjh5NPnjzZ6jWVi/999xfi8BerHjrUne/OPy8CRPvsF198cSElSgyphy4i0s5oQBcRSYhCFom+n+RukqvDj4mZ9iUiIq2nkEWibwFwxMweyPpg7aiH7l+j7E/s5Pe7/Ymcpk6dGtlnuoWM4+ZHP/pRZNtDDz3kZH/RDH/x6vbM/3/TqVMnJ6dbcHvQoEFOvuyyy5w8YcIEJ/sThKV7z4PES7Y99Gymz60FUBvePkyyaZFoERGJkZx66N4i0QDwZZJrSU4n2beZ59xFcjnJ5QVVKiIiLSpkkeiHAZwLoBrBK/h/T/c8LRItIlIaeS8S7d1fBWCumbV4AWxSe+h+nxOILn4wcqT7++zWW2918m233Vb8wlrB0aNHnfzd737XybNmzYo8Z9euXU72F7iQwvgLn3Tr1s3J/hxAF1xwgZPHjXMXHvOvaweii3dIaRXtOvTmFokOT5Y2+TSA9fkUKiIixVHIItG3kawGYAB2AvhCq1QoIiJZKWSR6JeLX46IiORLc7lkwe9R+tcCp5s7w593xb+uvHfv3kWqrnUtXLjQyf7c5YsWLXLyu+++G9lHkudmaQv8a9s7d+7s5L593QvUqqqqIvvwr22/9tprnTx+/Hgnx2Xu/aTQXC4iIu2MBnQRkYTQgC4ikhAa0EVEEkInRRFddKB79+5OHjJkiJP9E0DpJtLynxNHy5Yti2ybOXOmk/2Tom+99ZaTjxw5UvzCpKz8iwCA6M+EvxDH+eef72R/sjn/zUtA9ESrNE8nRUVE2hkN6CIiCaEBXUQkIRLfQ8+00AQAVFZWOtnv/02ZMsXJo0ePLlJ1rWvVqlVOfuqpp5z8i1/8IvKc7du3O/nQoUNO1puEBMi8EEevXr2cfNZZZ0X24S+A7f9c+XnEiBE515kU6qGLiLQzGtBFRBIim+lzu5D8Dck14SLR3w63Dya5lOR2kk+T7JxpXyIi0nqyWSSaALqb2ZFwoYtfAfgKgK8BeM7MZpF8BMAaM3s4w75avYfuTzzkXy+b7tpXf3GJm2++ufiFtQK/3/3EE084ecGCBU7eunWrk/3+OKAeubQOv+cORH9W/Unv/J/dwYMHO7m6ujqyT38hmYsuusjJ55xzTuZiY2D37t1OrqysLE4P3QJN7x7pFH4YgLEAZofbZwCYlEvBIiJSXFn10El2CBe3qAMwH8BbAOrNrDF8SA2Agc08V4tEi4iUQFYDupmdNLNqAJUARgEYlu0BtEi0iEhpZLME3QfMrJ7kIgBXAOhDsmP4Kr0SwO6Wn10cHTu6Jft9t+HDhzt50iS3E+RfUw5E56mIg/feey+ybdq0aU6eO3eukzds2ODk+vp6Jzc2NkKkHNKdq2toaHDy3r17nVxXV+fkzZs3O/mXv/xlZJ/+eOAv3nHGGWc42V9Au3///i0+HwC6du3qZP9zO3jwoJNramqcvG3btsg+/fNh/mLs2crmKpd+JPuEt7sCGA9gE4BFAD4TPux2AC/kVYGIiBRFNq/QBwCYQbIDgl8Az5jZXJIbAcwi+c8AVgGY1tJORESkdWWzSPRaAJek2b4DQT9dRERiIFZzufjzkgNAjx49nOzP/zBhwgQnf/7zn3fyRz7ykZxqLJcZM2Y4efbs2ZHHrFixwsn79u1z8okTJ4pfmEgb5l//7md/zPHngk83N7y/j1OnTjnZfy+Hf+7Kf3yWNJeLiEh7ogFdRCQhNKCLiCSEBnQRkYTI6Y1FxZB6EsKfnMdfaAIAxowZ4+Q777zTyaNGtY0LbV577TUn+ydB33zzTSfv2bMnso/f//73xS9MJMH8iz787J+gbOtvvtMrdBGRhNCALiKSEBrQRUQSoqQ99G7dumHYsD9M1DhgwADn/s9+9rOR53zuc59r9boKtXbt2sg2fyKthQsXOvntt992sj8ZTynf8CUiyaBX6CIiCaEBXUQkIQpZJPoJkm+TXB1+RBf4ExGRksmmh94AYGzqItEkXwnv+3szi84i1YzBgwdj5syZH+Tzzz8/p2LLxZ94/9FHH3Xyyy+/HHnOpk2bnOwvyJznBD0iIs3KZvpcA5BukWgREYmRvBaJNrOl4V3/QnItyf8keVozz/1gkeh0y6qJiEhx5LVINMmLAdyHYLHoywCcDuCeZp77wSLR6dbnExGR4sh3kegJZvZAuLmB5OMA7s70/C5dusSyb+7P3/D44487+dlnn3XyqlWrnHzgwIGM+xQRaW35LhK9meSAcBsBTAKwvjULFRGRlhWySPRCkv0AEMBqAH/ZinWKiEgGhSwSPbZVKhIRkbyUfD70cps7d25k25NPPunkJUuWONm/Dv348eNO1rwrIhIHeuu/iEhCaEAXEUkIDegiIgmhAV1EJCESd1J06dKlTv7xj3/s5MWLF0ees3PnTicfO3bMyTrpKSJtgV6hi4gkhAZ0EZGE0IAuIpIQba6H7k/B+9hjjzl53rx5Tl6zZk2LzweAkydPFqk6EZHy0St0EZGE0IAuIpIQGtBFRBKCpbzGmuS7AHYBqACwr2QHzp/qLK62UGdbqBFQncUW9zoHmVm/TA8q6YD+wUHJ5WY2suQHzpHqLK62UGdbqBFQncXWVurMRC0XEZGE0IAuIpIQ5RrQ/7dMx82V6iyutlBnW6gRUJ3F1lbqbFFZeugiIlJ8armIiCSEBnQRkYQo+YBOcgLJLSS3k7y31MdvDsnpJOtIrk/ZdjrJ+SS3hf/2LXONZ5NcRHIjyQ0kvxLTOruQ/A3JNWGd3w63Dya5NPzeP02ycznrbEKyA8lVJOeGOXZ1ktxJch3J1SSXh9ti9X0Pa+pDcjbJzSQ3kbwiTnWSHBp+DZs+DpH8apxqLERJB3SSHQD8N4DrAFwI4DaSF5ayhhY8AWCCt+1eAK+b2RAAr4e5nBoB/J2ZXQjgcgBfCr9+cauzAcBYMxsOoBrABJKXA/g3AP9pZucBeA/A1DLWmOorADal5LjWebWZVadcLx237zsA/ADAq2Y2DMBwBF/X2NRpZlvCr2E1gEsBHAUwJ041FsTMSvYB4AoA81LyfQDuK2UNGeqrArA+JW8BMCC8PQDAlnLX6NX7AoDxca4TQDcAKwF8DME78Tqm+79QxvoqEfwAjwUwFwBjWudOABXetlh93wH0BvA2wost4lpnSl3XAvi/ONeY60epWy4DAbyTkmvCbXHV38xqw9t7APQvZzGpSFYBuATAUsSwzrCNsRpAHYD5AN4CUG9mjeFD4vK9/y8A/wDgVJjPQDzrNACvkVxB8q5wW9y+74MBvAvg8bCF9RjJ7ohfnU1uBTAzvB3XGnOik6JZsuBXdyyu8STZA8CzAL5qZodS74tLnWZ20oI/aysBjAIwrMwlRZD8EwB1Zrai3LVkYbSZjUDQrvwSyT9OvTMm3/eOAEYAeNjMLgHwPrzWRUzqRHhe5HoAP/Pvi0uN+Sj1gL4bwNkpuTLcFld7SQ4AgPDfujLXA5KdEAzmPzWz58LNsauziZnVA1iEoHXRh2TToipx+N5fBeB6kjsBzELQdvkB4lcnzGx3+G8dgp7vKMTv+14DoMbMmlZqn41ggI9bnUDwi3Glme0NcxxrzFmpB/RlAIaEVxF0RvAnz4slriEXLwK4Pbx9O4KeddmQJIBpADaZ2X+k3BW3OvuR7BPe7oqgz78JwcD+mfBhZa/TzO4zs0ozq0Lwf3GhmU1GzOok2Z1kz6bbCHq/6xGz77uZ7QHwDsmh4aZxADYiZnWGbsMf2i1APGvMXRlOREwEsBVBT/Ufy30SIaWumQBqAZxA8EpjKoJ+6usAtgFYAOD0Mtc4GsGfgmsBrA4/Jsawzo8CWBXWuR7AN8Pt5wD4DYDtCP7UPa3c3/eUmscAmBvHOsN61oQfG5p+buL2fQ9rqgawPPzePw+gb9zqBNAdwH4AvVO2xarGfD/01n8RkYTQSVERkYTQgC4ikhAa0EVEEkIDuohIQmhAFxFJCA3oIiIJoQFdRCQh/h/OEj4xYf81FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show examples of the ground truth image and the reconstruction after\n",
    "# a pass through the autoencoder\n",
    "def get_example_outputs(model, data_loader):\n",
    "    model.eval()\n",
    "    ground_truth = []\n",
    "    model_output = []\n",
    "    with torch.no_grad():\n",
    "        for image, attribute, name in data_loader:\n",
    "            image = image.cuda()\n",
    "            recon, mu, var, r = model(image)\n",
    "            model_output.append(recon)\n",
    "            ground_truth.append(image)\n",
    "            if len(model_output) == 2:\n",
    "                return model_output, ground_truth\n",
    "            \n",
    "model_out, gt = get_example_outputs(model, val_loader)\n",
    "for idx in range(2):\n",
    "    plt.figure()\n",
    "    plt.title(\"Reconstruction \" + str(idx))\n",
    "    model_out[idx] = model_out[idx].view(model_out[idx].shape[1], model_out[idx].shape[2], model_out[idx].shape[3])\n",
    "    plt.imshow(model_out[idx].permute([1, 2, 0]).cpu().numpy())\n",
    "    plt.figure()\n",
    "    plt.title(\"Original Image \" + str(idx))\n",
    "    gt[idx] = gt[idx].view(gt[idx].shape[1], gt[idx].shape[2], gt[idx].shape[3])\n",
    "    plt.imshow(gt[idx].permute([1, 2, 0]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test L2 Score: 33.373837\n",
      "Test MSE Score: 0.0031959342\n"
     ]
    }
   ],
   "source": [
    "test_recon_score, test_mse_score = get_score_model(best_model, test_loader)\n",
    "print('Test L2 Score: ' + str(test_recon_score))\n",
    "print('Test MSE Score: ' + str(test_mse_score))"
   ]
  }
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