truss_5x5_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: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (0.10.0)\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: 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: numpy>=1.10.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (1.16.0)\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: 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: PyWavelets>=0.4.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.0.3)\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: 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: 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: imageio>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.5.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from PyWavelets>=0.4.0->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: 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: 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: 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: 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: six>=1.5 in /home/u0579755/env_dir/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.12.0)\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",
      "\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: scipy>=0.13.3 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-learn) (1.2.1)\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",
      "\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: 0.6963798701763153\n",
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      "Validation Reconstruction Score: 44.14707\n",
      "Validation MSE Score: 0.005285178\n",
      "\n",
      "Epoch: 1\n",
      "Total Loss: 0.6189386572156633\n",
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      "Validation Reconstruction Score: 40.606785\n",
      "Validation MSE Score: 0.0054559764\n",
      "\n",
      "Epoch: 2\n",
      "Total Loss: 0.5733882699693952\n",
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      "Trainig Reconstruction Score: 39.073353\n",
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      "Validation Reconstruction Score: 38.975952\n",
      "Validation MSE Score: 0.005183201\n",
      "\n",
      "Epoch: 3\n",
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      "Validation Reconstruction Score: 37.21239\n",
      "Validation MSE Score: 0.0055677467\n",
      "\n",
      "Epoch: 4\n",
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      "Validation Reconstruction Score: 36.50454\n",
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      "\n",
      "Epoch: 5\n",
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      "Validation Reconstruction Score: 35.997242\n",
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      "\n",
      "Epoch: 6\n",
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      "Validation Reconstruction Score: 35.666523\n",
      "Validation MSE Score: 0.004895118\n",
      "\n",
      "Epoch: 7\n",
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      "Validation Reconstruction Score: 35.431343\n",
      "Validation MSE Score: 0.004854106\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 8\n",
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      "Validation Reconstruction Score: 35.134636\n",
      "Validation MSE Score: 0.0047913864\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 9\n",
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      "Validation Reconstruction Score: 35.009743\n",
      "Validation MSE Score: 0.00492648\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 10\n",
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      "Validation Reconstruction Score: 34.938705\n",
      "Validation MSE Score: 0.0046479558\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 11\n",
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      "Validation Reconstruction Score: 34.824577\n",
      "Validation MSE Score: 0.004778277\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 12\n",
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      "Validation Reconstruction Score: 34.752205\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 13\n",
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      "Validation Reconstruction Score: 34.82513\n",
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      "\n",
      "Epoch: 14\n",
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      "\n",
      "Epoch: 15\n",
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      "Validation Reconstruction Score: 34.630936\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 16\n",
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      "Validation Reconstruction Score: 34.41709\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 17\n",
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      "\n",
      "Epoch: 18\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 19\n",
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      "\n",
      "Epoch: 20\n",
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      "New Best Model Saved!\n",
      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 22\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 23\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 25\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 26\n",
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     "text": [
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      "Validation Reconstruction Score: 33.956207\n",
      "Validation MSE Score: 0.0043062824\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 27\n",
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      "\n",
      "Epoch: 28\n",
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      "\n",
      "Epoch: 29\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 32\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: 39\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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      "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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      "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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      "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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      "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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      "\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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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 103\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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      "\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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      "\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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      "Validation Reconstruction Score: 33.393654\n",
      "Validation MSE Score: 0.0040033446\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 151\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 152\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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     "text": [
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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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      "\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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     "text": [
      "Validation Reconstruction Score: 33.391434\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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      "Training MSE Score: 0.003950646\n",
      "Validation Reconstruction Score: 33.387955\n",
      "Validation MSE Score: 0.0040233983\n",
      "\n",
      "Epoch: 193\n",
      "Total Loss: 0.40392888103212626\n",
      "Reconstruction Loss: 0.39797979550702234\n",
      "KL Loss: 0.001986059787616666\n",
      "Regression Loss: 0.003963026126647102\n",
      "Trainig Reconstruction Score: 32.916435\n",
      "Training MSE Score: 0.0039562085\n",
      "Validation Reconstruction Score: 33.373207\n",
      "Validation MSE Score: 0.0039983564\n",
      "\n",
      "Epoch: 194\n",
      "Total Loss: 0.4039014514003481\n",
      "Reconstruction Loss: 0.39793738637651715\n",
      "KL Loss: 0.0019866429951174982\n",
      "Regression Loss: 0.00397742438944988\n",
      "Trainig Reconstruction Score: 32.917053\n",
      "Training MSE Score: 0.0039523593\n",
      "Validation Reconstruction Score: 33.39088\n",
      "Validation MSE Score: 0.0040427186\n",
      "\n",
      "Epoch: 195\n",
      "Total Loss: 0.4039522179535457\n",
      "Reconstruction Loss: 0.39799406571047646\n",
      "KL Loss: 0.0019876275710495454\n",
      "Regression Loss: 0.003970525764660644\n",
      "Trainig Reconstruction Score: 32.917248\n",
      "Training MSE Score: 0.0039455816\n",
      "Validation Reconstruction Score: 33.37781\n",
      "Validation MSE Score: 0.003935886\n",
      "\n",
      "Epoch: 196\n",
      "Total Loss: 0.4038803628512791\n",
      "Reconstruction Loss: 0.39792472422122954\n",
      "KL Loss: 0.0019857173868721087\n",
      "Regression Loss: 0.00396992028524567\n",
      "Trainig Reconstruction Score: 32.917118\n",
      "Training MSE Score: 0.003941608\n",
      "Validation Reconstruction Score: 33.3811\n",
      "Validation MSE Score: 0.003964937\n",
      "\n",
      "Epoch: 197\n",
      "Total Loss: 0.40392466613224576\n",
      "Reconstruction Loss: 0.39797881203038354\n",
      "KL Loss: 0.001986373751424253\n",
      "Regression Loss: 0.003959481509601963\n",
      "Trainig Reconstruction Score: 32.91714\n",
      "Training MSE Score: 0.0039523863\n",
      "Validation Reconstruction Score: 33.38047\n",
      "Validation MSE Score: 0.0040036924\n",
      "\n",
      "Epoch: 198\n",
      "Total Loss: 0.40392602128641947\n",
      "Reconstruction Loss: 0.397971300142152\n",
      "KL Loss: 0.0019851535059777752\n",
      "Regression Loss: 0.003969566208044333\n",
      "Trainig Reconstruction Score: 32.913006\n",
      "Training MSE Score: 0.003940784\n",
      "Validation Reconstruction Score: 33.382374\n",
      "Validation MSE Score: 0.0039997385\n",
      "\n",
      "Epoch: 199\n",
      "Total Loss: 0.40388298758438657\n",
      "Reconstruction Loss: 0.39792741196496145\n",
      "KL Loss: 0.0019852677726053766\n",
      "Regression Loss: 0.003970310407956796\n",
      "Trainig Reconstruction Score: 32.913902\n",
      "Training MSE Score: 0.003942229\n",
      "Validation Reconstruction Score: 33.380215\n",
      "Validation MSE Score: 0.0039922562\n"
     ]
    }
   ],
   "source": [
    "# VAE conv kernel size = 5 z_dim=4\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=5, 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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\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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37e6NrVNuqqGLiOxlNKCLiKREsU2iLyW5huTi6Gv+YNsQEZHKK7ZJ9KUAdpjZDwvZ2VCtoYf+9m+/4uJx4zpK3mZvcE3zkuefj61z1733lryfoWjWrCNjy046xU/CNHL4aBeHP9c9wX0BWzdvdvHqjWHbXKC1wdfZm1t9jXzkSN9gZESL78jVGExkBgAtwTZiTR7CanRQY89Vcg+r1WETl3q4tr1exK6nL+K92ROcb7npphtc/Gbn6sITS1DpJtEiIlJnSqmhf43ki1FJZuxAK6lJtIhIdRQ7oF8F4GAAswGsBfCvA62oJtEiItVRVIMLM1vf/5jk1QD2quLuj3/8/1w8cdzU2Dpf+uoXXZw0H0x4HfXMo4+OrbNhw3YXP/nMgtg6adSa43ru3p6+IPbX9Xf3+Pr3zh3vuvjtrTtdPLIt/l/Bev2ytiY/5wzon+8N6rPdXbtj23x7i7+u+aVFz7p43fo1Lh67/2QXHxw0LQGA/Q+e7uLRo/0fzA2Nqqm/r/TvvSW43+Ccs8918UOP+Eb2C5+Knw+rlKKO0Elmt5//cwBLBlpXRESqI/EIPWoSPRdAO8nVAL4HYC7J2chMstYJ4MsVzFFERPJQbJPoayqQi4iIlGDIN4muB+u2xK87/f73v+/ir37l6y5u7xjwwiAAuefSPum0j7n40BkHufi2X93m4l3bdwy6j6GCTd2xZVs2bXOxjfNzpGx9219nvnbdKhf3vOtr8CNH+WvKAWD0eD9fzPAx/lp3v0cAwWe28523Y9u87a7bg3UGv/JrzQb/feSqbU6e7JeeMu+TLp4yaaKLhw3z5yT2ppp6Jb7VxqBZ9bxTPuXiqRP2d/Gv7rq7/ElEdOu/iEhKaEAXEUkJDegiIimhAV1EJCXU4KJGPvLx41186sfnubiYE1W7dvoTbAsf9w1vH1v4dMHbrIXGFn+68bg58ZusWptHuXj4MH8X8pZtW13c+cYyF7dP9CcKjz4mPu/R2H3G+H22+oYWRv9/Z+cuf7PSr2+Ln/zqXLsytqzSxk8c6eIT53zcxYdOn+ni1mACMWDvOnFaqtjEcMFNby+/9FLsNXf/+teDblMNLkRE9jIa0EVEUkIDuohISujGohp5csFTLm7s8r9b5556cuw1SRN8tbb5yaM+ctJcF088yN+IdOtNv0xKsyZ6+3wziuXLl8fW2We4n6SqeZivW45r8TcFzfrIcS6eNmU/v70R8RuLmhp9LZ/B+9/V5ZtIv7rUN/auRb08l83r/A1md97zG79CEJ8279TYNqYf4ScA22cff5NVQ0PwXg3RmrvBwgUlC288OnzGEbF1PtHjm2b89v4Hi9qXjtBFRFJCA7qISErk0yR6P5KPknyF5MskvxEtH0fyIZKvR/8OPjmJiIhUVD5NoicBmGRmi0iOAvAcgDMA/DWALWZ2OcmLAIw1swsH25auQy/NBRd808WjR8frvoPpDRol79wRnxhq0Yt+oqcFjxRXyyvECcd92MWto319duM63/QBAHZt8ZNz7W70DZmPmnmIiw+cdqiLRwUNnhubk08nhe/f8uUrXHzrrTcnbiMtzj77TBdPnOQnoGpr9fcF5Dr/M1Tr7IUKR1gLGsIDQFfQePr5Rc+5+KMnnFi2JtFrzWxR9Hg7gKUApgD4LIDrotWuQ2aQFxGRGimohk5yGoCjASwEMMHM1kZPrQMwYYDXqEm0iEgV5D2gkxwJ4FcALjAz9/euZeo2OWs3ahItIlIdeV2HTrIZmcH8RjO7I1q8nuQkM1sb1dk3VCpJybjyyitc/KXzvuTiCZP8H0mNwXXUYR1z5KgRsX18+INH+W3QNzp+5HeP5ZfsIKZOmeLiI476gIubmnzeo8eMi21j905/bXVzg6/Hdkye5OLhw/18Jgzem94cdc2d2/3cLE8Hdc2Ff/Bz5exNbrnRz1Nz4sd885XDZvprrUfv499/AGhu9uc9wpp6Yo09x9MsQxPocgszCu9nAICWFn8PyYwjZ8bWyUc+V7kQmZZzS83s37KeugdAf7vrcwFUrg2HiIgkyucI/aMAzgHwEsnF0bK/A3A5gFtJngfgTQB/WZkURUQkH/k0if4Dcv5xAwA4pbzpiIhIsTQfeooccfg0F3/mjM+5OJ/mwOHPQ2+vn8t561bftHjz2nUubh4Wr5W2BfXT4cN97b6xwdev93T5ffbu8tfoAsDuYC7y5kZfPRy1j7/OvDm4zrwvmC9m41vrY/u49oZfxJZJcY4P5v8HgA8eOdvFI0YN/pkNfFyZtUZCHT62hTq9Fn5PcF16W2ub5kMXEdmbaEAXEUkJDegiIimhAV1EJCV0UjTFJk70TSBOnz/fxZM64rM1NAY3e4T6wpOmPd0uznWOqTGYOKuvz29j23Y/0db6Df4EZV+33wcAtI32DZzHjfEn1Ea0+ROvYVorV3W6+MYbbortQ6pr1MR2F//F6Z928dhx/jNva4nfed4Q3JQWfu5JTWLqRVe3Pyna2qKToiIiexUN6CIiKaEBXUQkJdQkOsXWrfPzpT395PMunv+peCPqtkb/I9EQ3LDTGN6o0eTr42bxSa56e/xNPHt2+wm/dr293cXDghtK2OvrogDQ1uLXGd7a5l8T1Pp3B3X4hU88G9um1Nb2dZtcfO2117p4/8N8k5JPnHhSbBtjxoY3Jw1+M129Ntloahz8XNZAdIQuIpISGtBFRFKilCbRl5JcQ3Jx9DU/aVsiIlI5+dTQewB8K7tJNMmHoueuMLMfVi49Kaf9D/CNJZqbhsXWCWvmSTXGhqBRBCzHMQJ9Xb15mP+xaxnjrxlv6fL1w4ZxfvJ/ABgeNCFuDK4v3r71HRc/8Ov7Xbx81Z/ieUpdW/na6y6+OogBYJ92X0M/6sgPunjWLN9MZWzQaD0+mVf8558Nla+7F1vbz2f63LUA1kaPt5PsbxItIiJ1pJQm0QDwNZIvkvw5ybEDvEZNokVEqqCUJtFXATgYwGxkjuD/Ndfr1CRaRKQ6im4SbWbrs56/GsC9FclQinbqKf463RnTD3FxU1P84y/0Ot3w2VwzA4X17YagIe745hYX9wUNm43xrYZ57enqcvGv7vU/jm+tWpkjM0mbbZv8vECPL3h00Hjc/ge4eP4JH3fx5KkTY/toafE/r5W4tj38P5CvoptEk8xuq/7nAJYUlYGIiJRFKU2izyI5G5mDsk4AX65IhiIikpdSmkTfV/50RESkWJrLZSgJfq3+zRe+4OLxE/z85sOC2nRDEdfPhtPlJ5UH86kfhnNSW4PfCfuCbeTY5rvvvuviO+970MVvrVTNXJJtWfmmi2/45fUubs5xnmnOhz7q4pbR/v9Zc3AvxqRJvg7f3t4RT4R+vqMVK5bnTjiBbv0XEUkJDegiIimhAV1EJCU0oIuIpIROitZIW3CC8sxz/AnOCfvGGzi3tYYnOUv8fZzjLiCrwLxDiTcnBc83BhN+hY2oAeCVpS+4eMUrLxWZncjAunt6YsueeGpBDTLJj47QRURSQgO6iEhKaEAXEUkJ1dArZN6801x8xPQjXLzP6JEubmjwdeOqNK/NsYt6aJkbTky0YmVnbJ37f/NQbJnI3k5H6CIiKaEBXUQkJfKZPreV5DMkX4iaRF8WLT+Q5EKSy0neQjLeoFJERKomnxr6HgAnm9mOqNHFH0jeD+B/ItMk+maSPwFwHjJdjFJn1gd8o9kTPvZhF48ZMyb2mrB5RFVq4kNUWDN/LWj+e8stN1czHZEhK/EI3TJ2RGFz9GUATgZwe7T8OgBnVCRDERHJS141dJKNUXOLDQAeAvAGgK1m1n8b1WoAUwZ4rZpEi4hUQV4Dupn1mtlsAFMBzAEwPd8dqEm0iEh1FHQdupltJfkogOMBjCHZFB2lTwWwphIJltvsww6JLTvmY74x7IQJ+7q4uanZxaqHF8aCLhl79uxx8S033OjizjWrK56TSBrlc5VLB8kx0eM2APMALAXwKIAzo9XOBXB3pZIUEZFk+RyhTwJwHclGZH4B3Gpm95J8BcDNJH8A4HkA11QwTxERSZBPk+gXARydY/kKZOrpIiJSB1I3l8sZnzvLxTMOPtDF4fXhgGrihQivGQ/jndvjVzI9+tiTLn5h8cLyJyYiuvVfRCQtNKCLiKSEBnQRkZTQgC4ikhJD7qTo177+DRePGzPaxeU4wRneCBMaqidRw++rt7cvts62HTtcvPr111z8wp/8xFkrlvpYRGpHR+giIimhAV1EJCU0oIuIpERd1dC/8j++ElvW3tHu4kLr10n18NwvCuKGOq2ZhzXx4Cafrj1dLl61YpWLn1vyQmyTry17pUzJiUi16QhdRCQlNKCLiKREKU2iryX5J5KLo6/ZlU9XREQGUkqTaAD4tpndPshrnQn7TsA3v37Be/GIUSPd841NjfluakBhyTysueeqqMcq5DUomYeTXHX39MTW2b71bRe/tmyFix965MHyJyYiQ0Y+0+cagFxNokVEpI4U1STazPrnP/1Hki+SvIJkywCvfa9J9ObNm8qUtoiIhIpqEk1yJoCLkWkW/SEA4wBcOMBr32sSPX58e65VRESkDIptEn26mf0wWryH5H8B+F9Jr29sasQ+Y0cnrVaSpMvUq1UeD69/D2vk23fsdPFrLy9z8YJHfh/b5q7eePMIEZF+xTaJfpXkpGgZAZwBYEklExURkcGV0iT6EZIdyBz0Lgbw3yuYp4iIJCilSfTJFclIRESKUldzueQjvF6yKjXxYKd95uvhe7r8nCkAsOatNS7+4xOLXPzaCs2ZIiLlpVv/RURSQgO6iEhKaEAXEUkJDegiIilR/ZOi2TfcFNFsudBXWHhGM8csND1Bs+Tdu3a7eGXnShev2ugbJy/8w+ICsxIRKT8doYuIpIQGdBGRlNCALiKSElWvofdl1dAbiqihx/mieDgpVk93r4vf3bkDoWWvL3fxkmXPuHjlCk37KyL1T0foIiIpoQFdRCQlNKCLiKQEw5pzRXdGbgTwJoB2AEOhMK08y2so5DkUcgSUZ7nVe54HmFlH0kpVHdDf2yn5rJkdW/UdF0h5ltdQyHMo5Agoz3IbKnkmUclFRCQlNKCLiKRErQb0n9Vov4VSnuU1FPIcCjkCyrPchkqeg6pJDV1ERMpPJRcRkZTQgC4ikhJVH9BJnk5yGcnlJC+q9v4HQvLnJDeQXJK1bBzJh0i+Hv07tsY57kfyUZKvkHyZ5DfqNM9Wks+QfCHK87Jo+YEkF0af/S0kh9Uyz34kG0k+T/LeKK67PEl2knyJ5GKSz0bL6upzj3IaQ/J2kq+SXEry+HrKk+Th0XvY/7WN5AX1lGMpqjqgk2wE8B8APglgBoCzSM6oZg6DuBbA6cGyiwD8zswOBfC7KK6lHgDfMrMZAI4D8NXo/au3PPcAONnMjgIwG8DpJI8D8M8ArjCzQwC8DeC8GuaY7RsAlmbF9ZrnSWY2O+t66Xr73AHgRwAeMLPpAI5C5n2tmzzNbFn0Hs4G8EEAuwDcWU85lsTMqvYF4HgAv82KLwZwcTVzSMhvGoAlWfEyAJOix5MALKt1jkG+dwOYV895AhgOYBGADyNzJ15Trp+FGuY3FZn/wCcDuBeZplj1mGcngPZgWV197gBGA/gToost6jXPrLxOA/BEPedY6Fe1Sy5TAKzKildHy+rVBDNbGz1eB2BCLZPJRnIagKMBLEQd5hmVMRYD2ADgIQBvANhqZj3RKvXy2V8J4DsA+vsQjkd95mkAHiT5HMnzo2X19rkfCGAjgP+KSlj/SXIE6i/Pfp8HcFP0uF5zLIhOiubJMr+66+IaT5IjAfwKwAVmti37uXrJ08x6LfNn7VQAcwBMr3FKMSQ/DWCDmT1X61zycIKZHYNMufKrJE/MfrJOPvcmAMcAuMrMjgawE0Hpok7yRHRe5DMAbgufq5cci1HtAX0NgP2y4qnRsnq1nuQkAIj+3VDjfECyGZnB/EYzuyNaXHd59jOzrQAeRaZ0MYZkf1OVevjsPwrgMyQ7AdyMTNnlR6i/PGFma6J/NyBT852D+vvcVwNYbWYLo/h2ZAb4essTyPxiXGRm66O4HnMsWLUH9D8CODS6imAYMn/y3FPlHApxD4Bzo8fnIlOzrhmSBHANgKVm9m9ZT9WaEMUmAAABBklEQVRbnh0kx0SP25Cp8y9FZmA/M1qt5nma2cVmNtXMpiHzs/iImZ2NOsuT5AiSo/ofI1P7XYI6+9zNbB2AVSQPjxadAuAV1FmekbPwfrkFqM8cC1eDExHzAbyGTE3172t9EiErr5sArAXQjcyRxnnI1FN/B+B1AA8DGFfjHE9A5k/BFwEsjr7m12GeswA8H+W5BMB3o+UHAXgGwHJk/tRtqfXnnpXzXAD31mOeUT4vRF8v9/+/qbfPPcppNoBno8/+LgBj6y1PACMAbAYwOmtZXeVY7Jdu/RcRSQmdFBURSQkN6CIiKaEBXUQkJTSgi4ikhAZ0EZGU0IAuIpISGtBFRFLi/wNAWVJNS+3DCQAAAABJRU5ErkJggg==\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, reg = 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: 33.334793\n",
      "Test MSE Score: 0.004784675\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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