truss_5x5_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: numpy>=1.10.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (1.16.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (2.7.5)\n",
      "Requirement already satisfied: 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: 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 in /home/u0579755/env_dir/lib/python3.6/site-packages (from cycler>=0.10->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: 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: 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: scipy>=0.17.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.2.1)\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: 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: 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: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.10.0)\n",
      "Requirement already satisfied: setuptools in /home/u0579755/env_dir/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image) (40.8.0)\n",
      "Requirement already satisfied: six>=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",
      "\u001b[33mYou are using pip version 19.0.3, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: scikit-learn in /home/u0579755/env_dir/lib/python3.6/site-packages (0.20.3)\n",
      "Requirement already satisfied: numpy>=1.8.2 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-learn) (1.16.0)\n",
      "Requirement already satisfied: scipy>=0.13.3 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-learn) (1.2.1)\n",
      "\u001b[33mYou are using pip version 19.0.3, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Install\n",
    "!pip3 install matplotlib\n",
    "!pip3 install scikit-image\n",
    "!pip3 install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import\n",
    "import os \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import zipfile \n",
    "from skimage import io \n",
    "from torch.utils.data import Dataset\n",
    "import torch \n",
    "import pandas as pd \n",
    "from torchvision import transforms\n",
    "import copy\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# System Check\n",
    "try:\n",
    "  import google.colab\n",
    "  from google.colab import drive\n",
    "  from google.colab import files\n",
    "  IN_COLAB = True\n",
    "except:\n",
    "  IN_COLAB = False\n",
    "try:\n",
    "    hostname = !hostname\n",
    "    if 'lab' in hostname[0] and '.eng.utah.edu' in hostname[0]:\n",
    "        IN_CADE = True\n",
    "    else:\n",
    "        IN_CADE = False\n",
    "except:\n",
    "    IN_CADE = False\n",
    "\n",
    "assert(not IN_CADE or not IN_COLAB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find GPU\n",
    "def define_gpu_to_use(minimum_memory_mb = 3800):\n",
    "    gpu_to_use = None\n",
    "    try: \n",
    "        os.environ['CUDA_VISIBLE_DEVICES']\n",
    "        print('GPU already assigned before: ' + str(os.environ['CUDA_VISIBLE_DEVICES']))\n",
    "        return\n",
    "    except:\n",
    "        pass\n",
    "    torch.cuda.empty_cache()\n",
    "    for i in range(16):\n",
    "        free_memory = !nvidia-smi --query-gpu=memory.free -i $i --format=csv,nounits,noheader\n",
    "        if free_memory[0] == 'No devices were found':\n",
    "            break\n",
    "        free_memory = int(free_memory[0])\n",
    "        if free_memory>minimum_memory_mb-500:\n",
    "            gpu_to_use = i\n",
    "            break\n",
    "    if gpu_to_use is None:\n",
    "        print('Could not find any GPU available with the required free memory of ' +str(minimum_memory_mb) + 'MB. Please use a different system for this assignment.')\n",
    "    else:\n",
    "        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_to_use)\n",
    "        print('Chosen GPU: ' + str(gpu_to_use))\n",
    "        x = torch.rand((256,1024,minimum_memory_mb-500)).cuda()\n",
    "        x = torch.rand((1,1)).cuda()\n",
    "        del x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen GPU: 0\n"
     ]
    }
   ],
   "source": [
    "# Reserve GPU\n",
    "if IN_CADE:\n",
    "    define_gpu_to_use()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Visualize Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Opens zip file and places data in datsets folder\n",
    "def open_zip(filename):\n",
    "    zip_ref = zipfile.ZipFile(filename, 'r')\n",
    "    zip_ref.extractall('datasets')\n",
    "    zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return indexes to split data by for train, val, and test\n",
    "# Splits are 70% train, 10% val, 20% test\n",
    "def get_split(array_to_split, split):\n",
    "    np.random.seed(0)\n",
    "    np.random.shuffle(array_to_split)\n",
    "    np.random.seed()\n",
    "    if split == 'train':\n",
    "        array_to_split = array_to_split[:int(len(array_to_split)*0.7)]\n",
    "    elif split == 'val':\n",
    "        array_to_split = array_to_split[int(len(array_to_split)*0.7):int(len(array_to_split)*0.8)]\n",
    "    elif split == 'test':\n",
    "        array_to_split = array_to_split[int(len(array_to_split)*0.8):]\n",
    "    return array_to_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset class for Truss (Bracket/Beam) dataset\n",
    "class TrussDataset(Dataset):\n",
    "    def __init__(self, csv_file, root_dir, split='train'):\n",
    "        self.root_dir = root_dir\n",
    "        self.split = split\n",
    "        # Get the split\n",
    "        all_attributes_frame = pd.read_csv(root_dir + csv_file)\n",
    "        ids = get_split(pd.unique(all_attributes_frame['ID']), split)\n",
    "        self.attributes_frame = all_attributes_frame[all_attributes_frame['ID'].isin(ids)]\n",
    "              \n",
    "    def __len__(self):\n",
    "        return len(self.attributes_frame)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        image_name = os.path.join(self.root_dir, self.attributes_frame.iloc[idx, 1])\n",
    "        image = io.imread(image_name)\n",
    "        image = image.astype(np.float32)\n",
    "        image = image[:,:,:3]\n",
    "        image = image/255\n",
    "        image = np.swapaxes(image, 0, 1)\n",
    "        image = np.swapaxes(image, 0, 2)\n",
    "        attributes = self.attributes_frame.iloc[idx, 2:].values\n",
    "        attributes = attributes.astype('float')\n",
    "        attribute_names = self.attributes_frame.columns[2:].values\n",
    "        return torch.FloatTensor(image), torch.FloatTensor(attributes), list(attribute_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open zip of dataset\n",
    "open_zip('trussData.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train, val, and test datasets\n",
    "train_dataset = TrussDataset('truss_data.csv', 'datasets/trussData/')\n",
    "val_dataset = TrussDataset('truss_data.csv', 'datasets/trussData/', split='val')\n",
    "test_dataset = TrussDataset('truss_data.csv', 'datasets/trussData/', split='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualing a few cases of the training set\n",
    "for batch_idx, (image, attribute, name) in enumerate(train_dataset):\n",
    "    if batch_idx%250 == 0: \n",
    "        plt.figure()\n",
    "        plt.title(str(name) + ' ' + str(attribute.cpu().numpy()))\n",
    "        plt.imshow(image[:,:,:].permute([1, 2, 0]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculates L2 for images and MSE for regression - want both close to 0\n",
    "def get_score_model(model, data_loader):\n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        distances = []\n",
    "        mse = []\n",
    "        for image, attribute, name in data_loader:\n",
    "            image = image.cuda()\n",
    "            recon, mu, logvar, reg = model(image)\n",
    "            # Reconstruction Score\n",
    "            euclidean_dist = torch.pow(torch.sum(torch.pow((image - recon), 2)), 0.5)\n",
    "            distances.append(euclidean_dist.cpu().numpy())\n",
    "            # Regression Score\n",
    "            y_true = attribute.cpu().numpy()\n",
    "            y_pred = reg.cpu().numpy()         \n",
    "            mse.append(mean_squared_error(y_true, y_pred))\n",
    "    \n",
    "    return np.mean(distances), np.mean(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classes to create VAE, ability to change kernel_size of filters for convolution layers\n",
    "# and the latent representation dim\n",
    "class ConvBlock(torch.nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size):\n",
    "        super(ConvBlock, self).__init__()\n",
    "        padding = int((kernel_size - 1) / 2)\n",
    "        self.conv = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=padding)\n",
    "        self.batch = torch.nn.BatchNorm2d(num_features=out_channels)\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.batch(x)\n",
    "        x = self.relu(x)\n",
    "        return x\n",
    "\n",
    "class TransBlock(torch.nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(TransBlock, self).__init__()\n",
    "        self.convT = torch.nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=2, stride=2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.convT(x)\n",
    "        return x\n",
    "\n",
    "class Flatten(torch.nn.Module):\n",
    "    def forward(self, x):\n",
    "        return x.view(x.size(0), -1)\n",
    "\n",
    "class UnFlatten(torch.nn.Module):\n",
    "    def forward(self, x):\n",
    "        return x.view(-1, 32, 5, 10) # see if there is a way to not have this hardcoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VAE with Regression - The reconstruction and regression are done from the\n",
    "# latent variables calculated from the reparametrization (distribution)\n",
    "class VAE_REG(torch.nn.Module):\n",
    "    def __init__(self, kernel_size, z_dim):\n",
    "        super(VAE_REG, self).__init__()\n",
    "        self.encoder = torch.nn.Sequential(\n",
    "        ConvBlock(in_channels=3, out_channels=8, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=8, out_channels=8, kernel_size=kernel_size),\n",
    "        torch.nn.MaxPool2d(kernel_size=2),\n",
    "        ConvBlock(in_channels=8, out_channels=16, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=16, out_channels=16, kernel_size=kernel_size),\n",
    "        torch.nn.MaxPool2d(kernel_size=2),\n",
    "        ConvBlock(in_channels=16, out_channels=32, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=32, out_channels=32, kernel_size=kernel_size),\n",
    "        torch.nn.MaxPool2d(kernel_size=2),\n",
    "        Flatten()) \n",
    "        \n",
    "        self.mu = torch.nn.Linear(1600, z_dim)\n",
    "        self.logvar = torch.nn.Linear(1600, z_dim)\n",
    "        self.fc = torch.nn.Linear(z_dim, 1600)\n",
    "        \n",
    "        self.decoder = torch.nn.Sequential(\n",
    "        UnFlatten(),\n",
    "        TransBlock(in_channels=32, out_channels=32),\n",
    "        ConvBlock(in_channels=32, out_channels=32, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=32, out_channels=16, kernel_size=kernel_size),\n",
    "        TransBlock(in_channels=16, out_channels=16),\n",
    "        ConvBlock(in_channels=16, out_channels=16, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=16, out_channels=8, kernel_size=kernel_size),\n",
    "        TransBlock(in_channels=8, out_channels=8),\n",
    "        ConvBlock(in_channels=8, out_channels=8, kernel_size=kernel_size),\n",
    "        ConvBlock(in_channels=8, out_channels=3, kernel_size=kernel_size))\n",
    "        \n",
    "        self.regression = torch.nn.Sequential(\n",
    "            torch.nn.Linear(in_features=z_dim, out_features=4),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(in_features=4, out_features=1))\n",
    "            \n",
    "    def reparametrize(self, mu, logvar):\n",
    "        std = logvar.mul(0.5).exp_()\n",
    "        esp = torch.cuda.FloatTensor(mu.size()).normal_()\n",
    "        z = mu + std * esp\n",
    "        return z\n",
    "    \n",
    "    def bottleneck(self, x):\n",
    "        mu, logvar = self.mu(x), self.logvar(x)\n",
    "        z = self.reparametrize(mu, logvar)\n",
    "        return z, mu, logvar\n",
    "    \n",
    "    def representation(self, x):\n",
    "        return self.bottleneck(self.encoder(x))[0]\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        z, mu, logvar = self.bottleneck(x) # z is latent representation\n",
    "        x = self.fc(z)\n",
    "        x = torch.sigmoid(self.decoder(x)) # VAE output\n",
    "        r = self.regression(z) # Regression output\n",
    "        return x, mu, logvar, r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function for this task, combines reconstruction and KL for the image task and the MSELoss\n",
    "# for the regression task\n",
    "def loss_function(reconstruction, image, mu, logvar, attributes, r, batch_size):\n",
    "    reconstruction_loss = torch.nn.functional.binary_cross_entropy(reconstruction, image)\n",
    "    kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "    kl_loss /=  batch_size * image.shape[1] * image.shape[2] * image.shape[3]\n",
    "    reg_loss =  torch.nn.MSELoss()\n",
    "    return reconstruction_loss, kl_loss, reg_loss(attributes, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Starting Training!\n",
      "\n",
      "Epoch: 0\n",
      "Total Loss: 0.7125790740762438\n",
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      "Validation Reconstruction Score: 44.267628\n",
      "Validation MSE Score: 0.0041164327\n",
      "\n",
      "Epoch: 1\n",
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      "Trainig Reconstruction Score: 41.178726\n",
      "Training MSE Score: 0.0055621024\n",
      "Validation Reconstruction Score: 41.43788\n",
      "Validation MSE Score: 0.0056494894\n",
      "\n",
      "Epoch: 2\n",
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      "Trainig Reconstruction Score: 38.96538\n",
      "Training MSE Score: 0.0048163463\n",
      "Validation Reconstruction Score: 38.93322\n",
      "Validation MSE Score: 0.004237454\n",
      "\n",
      "Epoch: 3\n",
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      "Validation Reconstruction Score: 37.72143\n",
      "Validation MSE Score: 0.0041063135\n",
      "\n",
      "Epoch: 4\n",
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      "Validation Reconstruction Score: 36.59667\n",
      "Validation MSE Score: 0.0031138964\n",
      "\n",
      "Epoch: 5\n",
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      "Validation Reconstruction Score: 35.995934\n",
      "Validation MSE Score: 0.0037324463\n",
      "\n",
      "Epoch: 6\n",
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      "Validation Reconstruction Score: 35.552143\n",
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      "\n",
      "Epoch: 7\n",
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      "Validation Reconstruction Score: 35.187733\n",
      "Validation MSE Score: 0.0033684487\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 8\n",
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      "Validation Reconstruction Score: 34.973873\n",
      "Validation MSE Score: 0.0031869947\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 9\n",
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      "Validation Reconstruction Score: 34.803543\n",
      "Validation MSE Score: 0.0033799487\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 10\n",
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      "Validation Reconstruction Score: 34.588497\n",
      "Validation MSE Score: 0.0032714126\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 11\n",
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      "Validation Reconstruction Score: 34.461613\n",
      "Validation MSE Score: 0.003566988\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 12\n",
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      "Validation MSE Score: 0.0030756793\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 13\n",
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      "Validation MSE Score: 0.003415029\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 14\n",
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      "Validation Reconstruction Score: 34.271763\n",
      "Validation MSE Score: 0.003170406\n",
      "\n",
      "Epoch: 15\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 16\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 17\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 18\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 19\n",
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      "Validation Reconstruction Score: 33.943974\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 21\n",
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      "\n",
      "Epoch: 22\n",
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      "Validation Reconstruction Score: 33.921913\n",
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      "\n",
      "Epoch: 23\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 24\n",
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      "\n",
      "Epoch: 25\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 26\n",
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     "text": [
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      "Training MSE Score: 0.0021601806\n",
      "Validation Reconstruction Score: 33.821354\n",
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      "\n",
      "Epoch: 27\n",
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      "\n",
      "Epoch: 28\n",
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      "Validation MSE Score: 0.0031810016\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 29\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: 34\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 40\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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      "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",
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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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      "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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      "\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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      "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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      "Validation MSE Score: 0.0037682836\n",
      "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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      "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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      "Validation MSE Score: 0.0037131882\n",
      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\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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      "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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      "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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      "Validation MSE Score: 0.0033703896\n",
      "\n",
      "Epoch: 192\n",
      "Total Loss: 0.39839241547243937\n",
      "Reconstruction Loss: 0.3951232544013432\n",
      "KL Loss: 0.003056598298384675\n",
      "Regression Loss: 0.00021256313001296283\n",
      "Trainig Reconstruction Score: 32.793755\n",
      "Training MSE Score: 0.00020121037\n",
      "Validation Reconstruction Score: 33.290085\n",
      "Validation MSE Score: 0.0035017955\n",
      "\n",
      "Epoch: 193\n",
      "Total Loss: 0.39840138597147806\n",
      "Reconstruction Loss: 0.3951237827539444\n",
      "KL Loss: 0.003058377666664975\n",
      "Regression Loss: 0.00021922805998266475\n",
      "Trainig Reconstruction Score: 32.795162\n",
      "Training MSE Score: 0.00019016048\n",
      "Validation Reconstruction Score: 33.297752\n",
      "Validation MSE Score: 0.0034560675\n",
      "\n",
      "Epoch: 194\n",
      "Total Loss: 0.3983817496470043\n",
      "Reconstruction Loss: 0.3951155581644603\n",
      "KL Loss: 0.0030569330057395355\n",
      "Regression Loss: 0.00020925566630986786\n",
      "Trainig Reconstruction Score: 32.79563\n",
      "Training MSE Score: 0.00020676013\n",
      "Validation Reconstruction Score: 33.28934\n",
      "Validation MSE Score: 0.0034804603\n",
      "\n",
      "Epoch: 195\n",
      "Total Loss: 0.39837314358779363\n",
      "Reconstruction Loss: 0.3951019304139273\n",
      "KL Loss: 0.0030576293256932073\n",
      "Regression Loss: 0.00021358336141150046\n",
      "Trainig Reconstruction Score: 32.79545\n",
      "Training MSE Score: 0.00020078493\n",
      "Validation Reconstruction Score: 33.297386\n",
      "Validation MSE Score: 0.0035200478\n",
      "\n",
      "Epoch: 196\n",
      "Total Loss: 0.39836887036051066\n",
      "Reconstruction Loss: 0.3950952031782695\n",
      "KL Loss: 0.0030574069025793246\n",
      "Regression Loss: 0.00021626191022992966\n",
      "Trainig Reconstruction Score: 32.79566\n",
      "Training MSE Score: 0.0001833179\n",
      "Validation Reconstruction Score: 33.306683\n",
      "Validation MSE Score: 0.0036268407\n",
      "\n",
      "Epoch: 197\n",
      "Total Loss: 0.3983917589698519\n",
      "Reconstruction Loss: 0.39512026693139757\n",
      "KL Loss: 0.0030580346638868963\n",
      "Regression Loss: 0.00021345736588825406\n",
      "Trainig Reconstruction Score: 32.79443\n",
      "Training MSE Score: 0.00018441096\n",
      "Validation Reconstruction Score: 33.293407\n",
      "Validation MSE Score: 0.0034087123\n",
      "\n",
      "Epoch: 198\n",
      "Total Loss: 0.39833223819732666\n",
      "Reconstruction Loss: 0.3950729855469295\n",
      "KL Loss: 0.003056613731730197\n",
      "Regression Loss: 0.0002026343402706386\n",
      "Trainig Reconstruction Score: 32.79625\n",
      "Training MSE Score: 0.0001819313\n",
      "Validation Reconstruction Score: 33.296413\n",
      "Validation MSE Score: 0.0034340927\n",
      "\n",
      "Epoch: 199\n",
      "Total Loss: 0.39834820670740945\n",
      "Reconstruction Loss: 0.39507925723280224\n",
      "KL Loss: 0.003058157951038863\n",
      "Regression Loss: 0.00021079110369360673\n",
      "Trainig Reconstruction Score: 32.795258\n",
      "Training MSE Score: 0.0001790594\n",
      "Validation Reconstruction Score: 33.286034\n",
      "Validation MSE Score: 0.003594261\n"
     ]
    }
   ],
   "source": [
    "# VAE conv kernel size = 5 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=5, 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 = 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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\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: 33.229668\n",
      "Test MSE Score: 0.003324734\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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