truss_3x3_zdim8.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: kiwisolver>=1.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (1.0.1)\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: 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: numpy>=1.10.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (1.16.0)\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",
      "Requirement already satisfied: six>=1.5 in /home/u0579755/env_dir/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib) (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-image in /home/u0579755/env_dir/lib/python3.6/site-packages (0.15.0)\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: imageio>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.5.0)\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: 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: scipy>=0.17.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.2.1)\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: 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: 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: 0.742022044318063\n",
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      "Validation Reconstruction Score: 44.783382\n",
      "Validation MSE Score: 0.0041827722\n",
      "\n",
      "Epoch: 1\n",
      "Total Loss: 0.6502300679683686\n",
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      "Training MSE Score: 0.004881492\n",
      "Validation Reconstruction Score: 42.529354\n",
      "Validation MSE Score: 0.0045954976\n",
      "\n",
      "Epoch: 2\n",
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      "Trainig Reconstruction Score: 41.193455\n",
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      "Validation Reconstruction Score: 41.377262\n",
      "Validation MSE Score: 0.003908126\n",
      "\n",
      "Epoch: 3\n",
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      "Validation Reconstruction Score: 40.092503\n",
      "Validation MSE Score: 0.0035181257\n",
      "\n",
      "Epoch: 4\n",
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      "Validation Reconstruction Score: 38.447823\n",
      "Validation MSE Score: 0.003915144\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 5\n",
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      "Validation Reconstruction Score: 37.070198\n",
      "Validation MSE Score: 0.0035390856\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 6\n",
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      "Validation Reconstruction Score: 36.38028\n",
      "Validation MSE Score: 0.0035070002\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 7\n",
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      "Validation Reconstruction Score: 35.969765\n",
      "Validation MSE Score: 0.0034020664\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 8\n",
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      "Validation Reconstruction Score: 35.675583\n",
      "Validation MSE Score: 0.003298261\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 9\n",
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      "Validation Reconstruction Score: 35.41113\n",
      "Validation MSE Score: 0.0033870977\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 10\n",
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      "Validation Reconstruction Score: 35.402035\n",
      "Validation MSE Score: 0.0033483268\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 11\n",
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      "Validation Reconstruction Score: 35.10314\n",
      "Validation MSE Score: 0.0034277898\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 12\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 13\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 14\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 15\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 16\n",
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      "Validation Reconstruction Score: 34.74801\n",
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      "\n",
      "Epoch: 17\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 18\n",
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      "\n",
      "Epoch: 19\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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      "Validation MSE Score: 0.0036081222\n",
      "\n",
      "Epoch: 23\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",
      "Epoch: 26\n"
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     "text": [
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      "Validation Reconstruction Score: 34.273983\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 27\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 28\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 29\n",
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      "\n",
      "Epoch: 30\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: 35\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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      "\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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      "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",
      "\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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      "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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      "Validation MSE Score: 0.0037874002\n",
      "New Best Model Saved!\n",
      "\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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      "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",
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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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      "\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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      "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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      "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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      "New Best Model Saved!\n",
      "\n",
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      "New Best Model Saved!\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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      "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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      "New Best Model Saved!\n",
      "\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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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "KL Loss: 0.003391288044596357\n",
      "Regression Loss: 0.00015173784299154898\n",
      "Trainig Reconstruction Score: 33.03917\n",
      "Training MSE Score: 0.00012790854\n",
      "Validation Reconstruction Score: 33.47127\n",
      "Validation MSE Score: 0.0039260727\n",
      "\n",
      "Epoch: 191\n",
      "Total Loss: 0.40465732557432993\n",
      "Reconstruction Loss: 0.40110413772719244\n",
      "KL Loss: 0.0033907547925731965\n",
      "Regression Loss: 0.00016243399129182632\n",
      "Trainig Reconstruction Score: 33.044666\n",
      "Training MSE Score: 0.000143787\n",
      "Validation Reconstruction Score: 33.487522\n",
      "Validation MSE Score: 0.00407345\n",
      "\n",
      "Epoch: 192\n",
      "Total Loss: 0.40465535862105234\n",
      "Reconstruction Loss: 0.4010928805385317\n",
      "KL Loss: 0.003392904980241188\n",
      "Regression Loss: 0.0001695734524089078\n",
      "Trainig Reconstruction Score: 33.035625\n",
      "Training MSE Score: 0.00012909507\n",
      "Validation Reconstruction Score: 33.461746\n",
      "Validation MSE Score: 0.0041426267\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 193\n",
      "Total Loss: 0.4046115632568087\n",
      "Reconstruction Loss: 0.4010660316262926\n",
      "KL Loss: 0.0033924297795497947\n",
      "Regression Loss: 0.00015310188926897744\n",
      "Trainig Reconstruction Score: 33.03455\n",
      "Training MSE Score: 0.00011581189\n",
      "Validation Reconstruction Score: 33.476517\n",
      "Validation MSE Score: 0.004032027\n",
      "\n",
      "Epoch: 194\n",
      "Total Loss: 0.40462931735174995\n",
      "Reconstruction Loss: 0.40106114063944137\n",
      "KL Loss: 0.0033933594224176238\n",
      "Regression Loss: 0.0001748147308847235\n",
      "Trainig Reconstruction Score: 33.033623\n",
      "Training MSE Score: 0.00012621126\n",
      "Validation Reconstruction Score: 33.472183\n",
      "Validation MSE Score: 0.0040349797\n",
      "\n",
      "Epoch: 195\n",
      "Total Loss: 0.4045961869614465\n",
      "Reconstruction Loss: 0.40105633182185035\n",
      "KL Loss: 0.003392221961569573\n",
      "Regression Loss: 0.00014763308728496277\n",
      "Trainig Reconstruction Score: 33.041862\n",
      "Training MSE Score: 0.00013878818\n",
      "Validation Reconstruction Score: 33.483383\n",
      "Validation MSE Score: 0.0042052823\n",
      "\n",
      "Epoch: 196\n",
      "Total Loss: 0.40460967464106423\n",
      "Reconstruction Loss: 0.4010530139718737\n",
      "KL Loss: 0.003391606134495565\n",
      "Regression Loss: 0.00016505014204345312\n",
      "Trainig Reconstruction Score: 33.035633\n",
      "Training MSE Score: 0.00012262487\n",
      "Validation Reconstruction Score: 33.47793\n",
      "Validation MSE Score: 0.004106702\n",
      "\n",
      "Epoch: 197\n",
      "Total Loss: 0.40457193340573994\n",
      "Reconstruction Loss: 0.4010217104639326\n",
      "KL Loss: 0.003392781313907887\n",
      "Regression Loss: 0.0001574417459518632\n",
      "Trainig Reconstruction Score: 33.03455\n",
      "Training MSE Score: 0.00013573653\n",
      "Validation Reconstruction Score: 33.473515\n",
      "Validation MSE Score: 0.004290428\n",
      "\n",
      "Epoch: 198\n",
      "Total Loss: 0.4045743784734181\n",
      "Reconstruction Loss: 0.4010303178003856\n",
      "KL Loss: 0.0033938774872305138\n",
      "Regression Loss: 0.00015018321199542178\n",
      "Trainig Reconstruction Score: 33.034893\n",
      "Training MSE Score: 0.000121878606\n",
      "Validation Reconstruction Score: 33.475487\n",
      "Validation MSE Score: 0.004173463\n",
      "\n",
      "Epoch: 199\n",
      "Total Loss: 0.4046216905117035\n",
      "Reconstruction Loss: 0.4010790871722358\n",
      "KL Loss: 0.0033930149195449694\n",
      "Regression Loss: 0.00014958909133773498\n",
      "Trainig Reconstruction Score: 33.037956\n",
      "Training MSE Score: 0.00011400665\n",
      "Validation Reconstruction Score: 33.46801\n",
      "Validation MSE Score: 0.004149865\n"
     ]
    }
   ],
   "source": [
    "# VAE conv kernel size = 3 z_dim=8\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=3, z_dim=8)\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 = 40 # 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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\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: 33.445168\n",
      "Test MSE Score: 0.003291025\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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