truss_7x7_zdim2.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: 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: python-dateutil>=2.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib) (2.7.5)\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: 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: imageio>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.5.0)\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: scipy>=0.17.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.2.1)\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: 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: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.7.5)\n",
      "Requirement already satisfied: 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 in /home/u0579755/env_dir/lib/python3.6/site-packages (from cycler>=0.10->matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.12.0)\n",
      "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: 0.7365141212940216\n",
      "Reconstruction Loss: 0.6579576339040484\n",
      "KL Loss: 0.015216188103503165\n",
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      "Trainig Reconstruction Score: 42.794823\n",
      "Training MSE Score: 0.005469131\n",
      "Validation Reconstruction Score: 42.99991\n",
      "Validation MSE Score: 0.0049476316\n",
      "\n",
      "Epoch: 1\n",
      "Total Loss: 0.6251249304839543\n",
      "Reconstruction Loss: 0.6119291475840978\n",
      "KL Loss: 0.006736446504614183\n",
      "Regression Loss: 0.0064593374662633455\n",
      "Trainig Reconstruction Score: 40.71143\n",
      "Training MSE Score: 0.006753414\n",
      "Validation Reconstruction Score: 40.94625\n",
      "Validation MSE Score: 0.007180597\n",
      "\n",
      "Epoch: 2\n",
      "Total Loss: 0.5903626927307674\n",
      "Reconstruction Loss: 0.5781197454248156\n",
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      "Regression Loss: 0.008455278045896972\n",
      "Trainig Reconstruction Score: 40.28756\n",
      "Training MSE Score: 0.009378132\n",
      "Validation Reconstruction Score: 40.272858\n",
      "Validation MSE Score: 0.010580914\n",
      "\n",
      "Epoch: 3\n",
      "Total Loss: 0.5616701790264674\n",
      "Reconstruction Loss: 0.5512253871985844\n",
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      "Trainig Reconstruction Score: 39.32625\n",
      "Training MSE Score: 0.0077631017\n",
      "Validation Reconstruction Score: 39.301075\n",
      "Validation MSE Score: 0.0069317603\n",
      "\n",
      "Epoch: 4\n",
      "Total Loss: 0.5472252718039922\n",
      "Reconstruction Loss: 0.5377826971667153\n",
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      "Regression Loss: 0.0069738611279587664\n",
      "Trainig Reconstruction Score: 38.322\n",
      "Training MSE Score: 0.005831585\n",
      "Validation Reconstruction Score: 38.208004\n",
      "Validation MSE Score: 0.005546849\n",
      "\n",
      "Epoch: 5\n",
      "Total Loss: 0.5376986069338662\n",
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      "Regression Loss: 0.0068735894996539825\n",
      "Trainig Reconstruction Score: 37.895584\n",
      "Training MSE Score: 0.0066131796\n",
      "Validation Reconstruction Score: 37.81976\n",
      "Validation MSE Score: 0.006638576\n",
      "\n",
      "Epoch: 6\n",
      "Total Loss: 0.5286183042185647\n",
      "Reconstruction Loss: 0.5209188721009663\n",
      "KL Loss: 0.0018792913328590137\n",
      "Regression Loss: 0.005820142151787877\n",
      "Trainig Reconstruction Score: 37.78799\n",
      "Training MSE Score: 0.0055778543\n",
      "Validation Reconstruction Score: 37.68905\n",
      "Validation MSE Score: 0.005454847\n",
      "\n",
      "Epoch: 7\n",
      "Total Loss: 0.5186336083071572\n",
      "Reconstruction Loss: 0.5116260873419898\n",
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      "Regression Loss: 0.005370993185455778\n",
      "Trainig Reconstruction Score: 37.337284\n",
      "Training MSE Score: 0.00521533\n",
      "Validation Reconstruction Score: 37.301212\n",
      "Validation MSE Score: 0.00485068\n",
      "\n",
      "Epoch: 8\n",
      "Total Loss: 0.515096932223865\n",
      "Reconstruction Loss: 0.5083005739109857\n",
      "KL Loss: 0.0015325951905521963\n",
      "Regression Loss: 0.0052637624171828585\n",
      "Trainig Reconstruction Score: 37.73701\n",
      "Training MSE Score: 0.005384661\n",
      "Validation Reconstruction Score: 37.61627\n",
      "Validation MSE Score: 0.0051449044\n",
      "\n",
      "Epoch: 9\n",
      "Total Loss: 0.5118864400046212\n",
      "Reconstruction Loss: 0.505104062812669\n",
      "KL Loss: 0.0015239164549192147\n",
      "Regression Loss: 0.005258456296620092\n",
      "Trainig Reconstruction Score: 37.37007\n",
      "Training MSE Score: 0.005019076\n",
      "Validation Reconstruction Score: 37.310276\n",
      "Validation MSE Score: 0.0043484904\n",
      "\n",
      "Epoch: 10\n",
      "Total Loss: 0.5088137796946934\n",
      "Reconstruction Loss: 0.5023274579218455\n",
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      "Regression Loss: 0.005056069490300226\n",
      "Trainig Reconstruction Score: 36.80857\n",
      "Training MSE Score: 0.0048043584\n",
      "Validation Reconstruction Score: 36.83653\n",
      "Validation MSE Score: 0.0042524845\n",
      "\n",
      "Epoch: 11\n",
      "Total Loss: 0.5029584488698414\n",
      "Reconstruction Loss: 0.4966295740434102\n",
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      "Regression Loss: 0.004917407940540995\n",
      "Trainig Reconstruction Score: 37.018024\n",
      "Training MSE Score: 0.00475591\n",
      "Validation Reconstruction Score: 37.05317\n",
      "Validation MSE Score: 0.003827659\n",
      "\n",
      "Epoch: 12\n",
      "Total Loss: 0.5011731773614884\n",
      "Reconstruction Loss: 0.4948231377771923\n",
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      "Regression Loss: 0.005003939587290266\n",
      "Trainig Reconstruction Score: 36.858192\n",
      "Training MSE Score: 0.004765121\n",
      "Validation Reconstruction Score: 37.016247\n",
      "Validation MSE Score: 0.004085788\n",
      "\n",
      "Epoch: 13\n",
      "Total Loss: 0.49716290831565857\n",
      "Reconstruction Loss: 0.49096730734620775\n",
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      "Regression Loss: 0.004809362484541322\n",
      "Trainig Reconstruction Score: 36.806847\n",
      "Training MSE Score: 0.004920842\n",
      "Validation Reconstruction Score: 36.872875\n",
      "Validation MSE Score: 0.004493748\n",
      "\n",
      "Epoch: 14\n",
      "Total Loss: 0.4933509511607034\n",
      "Reconstruction Loss: 0.48718228680746894\n",
      "KL Loss: 0.0013608860583709818\n",
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      "Trainig Reconstruction Score: 36.523975\n",
      "Training MSE Score: 0.0047335806\n",
      "Validation Reconstruction Score: 36.592884\n",
      "Validation MSE Score: 0.004160431\n",
      "\n",
      "Epoch: 15\n",
      "Total Loss: 0.4905300029686519\n",
      "Reconstruction Loss: 0.48429412799222127\n",
      "KL Loss: 0.001362917825047459\n",
      "Regression Loss: 0.004872958268970251\n",
      "Trainig Reconstruction Score: 36.167156\n",
      "Training MSE Score: 0.004841763\n",
      "Validation Reconstruction Score: 36.28938\n",
      "Validation MSE Score: 0.00442564\n",
      "\n",
      "Epoch: 16\n",
      "Total Loss: 0.49154466433184485\n",
      "Reconstruction Loss: 0.4852156260183879\n",
      "KL Loss: 0.0014183008427997785\n",
      "Regression Loss: 0.00491073624663321\n",
      "Trainig Reconstruction Score: 36.124233\n",
      "Training MSE Score: 0.00466444\n",
      "Validation Reconstruction Score: 36.371727\n",
      "Validation MSE Score: 0.0040432694\n",
      "\n",
      "Epoch: 17\n",
      "Total Loss: 0.488679090993745\n",
      "Reconstruction Loss: 0.4824209596429552\n",
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      "Regression Loss: 0.004846185854902225\n",
      "Trainig Reconstruction Score: 36.14723\n",
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      "Validation Reconstruction Score: 36.228138\n",
      "Validation MSE Score: 0.004253199\n",
      "\n",
      "Epoch: 18\n",
      "Total Loss: 0.484475799543517\n",
      "Reconstruction Loss: 0.4783777356147766\n",
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      "Regression Loss: 0.004732643144338258\n",
      "Trainig Reconstruction Score: 36.22765\n",
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      "Validation Reconstruction Score: 36.3978\n",
      "Validation MSE Score: 0.0044721877\n",
      "\n",
      "Epoch: 19\n",
      "Total Loss: 0.48308198877743314\n",
      "Reconstruction Loss: 0.47680858884538924\n",
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      "Trainig Reconstruction Score: 36.096466\n",
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      "Validation Reconstruction Score: 36.234608\n",
      "Validation MSE Score: 0.0049662753\n",
      "\n",
      "Epoch: 20\n",
      "Total Loss: 0.4810779379946845\n",
      "Reconstruction Loss: 0.47477858534881046\n",
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      "Regression Loss: 0.004906946799850889\n",
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      "Validation Reconstruction Score: 36.041058\n",
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      "\n",
      "Epoch: 21\n",
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      "Validation Reconstruction Score: 35.879257\n",
      "Validation MSE Score: 0.0045596026\n",
      "\n",
      "Epoch: 22\n",
      "Total Loss: 0.47652958716664995\n",
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      "Validation Reconstruction Score: 35.793365\n",
      "Validation MSE Score: 0.003715503\n",
      "\n",
      "Epoch: 23\n",
      "Total Loss: 0.47218619968209946\n",
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      "Training MSE Score: 0.0047201216\n",
      "Validation Reconstruction Score: 35.871666\n",
      "Validation MSE Score: 0.0037546298\n",
      "\n",
      "Epoch: 24\n",
      "Total Loss: 0.4775233592305865\n",
      "Reconstruction Loss: 0.47127923369407654\n",
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      "Training MSE Score: 0.004651783\n",
      "Validation Reconstruction Score: 36.027775\n",
      "Validation MSE Score: 0.0039154924\n",
      "\n",
      "Epoch: 25\n",
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      "\n",
      "Epoch: 26\n",
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      "Validation Reconstruction Score: 35.77153\n",
      "Validation MSE Score: 0.00365263\n",
      "\n",
      "Epoch: 27\n",
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     "text": [
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      "Training MSE Score: 0.004848995\n",
      "Validation Reconstruction Score: 35.74538\n",
      "Validation MSE Score: 0.00424975\n",
      "\n",
      "Epoch: 28\n",
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      "Validation MSE Score: 0.0040007727\n",
      "\n",
      "Epoch: 29\n",
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      "Validation Reconstruction Score: 35.72455\n",
      "Validation MSE Score: 0.0036583291\n",
      "\n",
      "Epoch: 30\n",
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      "Validation MSE Score: 0.0037594393\n",
      "\n",
      "Epoch: 31\n",
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      "Validation MSE Score: 0.004141529\n",
      "\n",
      "Epoch: 32\n",
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      "\n",
      "Epoch: 33\n",
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      "\n",
      "Epoch: 34\n",
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      "Validation Reconstruction Score: 35.545994\n",
      "Validation MSE Score: 0.0037156353\n",
      "\n",
      "Epoch: 35\n",
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      "Validation Reconstruction Score: 35.338875\n",
      "Validation MSE Score: 0.003578696\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 36\n",
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      "Validation Reconstruction Score: 35.50311\n",
      "Validation MSE Score: 0.0038606878\n",
      "\n",
      "Epoch: 37\n",
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      "Validation Reconstruction Score: 35.259834\n",
      "Validation MSE Score: 0.0037195452\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 38\n",
      "Total Loss: 0.4575143273387636\n",
      "Reconstruction Loss: 0.45138110646179747\n",
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      "Validation Reconstruction Score: 35.31555\n",
      "Validation MSE Score: 0.0045182197\n",
      "\n",
      "Epoch: 39\n",
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      "Validation Reconstruction Score: 36.382694\n",
      "Validation MSE Score: 0.004659342\n",
      "\n",
      "Epoch: 40\n",
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      "\n",
      "Epoch: 41\n",
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      "\n",
      "Epoch: 42\n",
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      "\n",
      "Epoch: 43\n",
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      "Validation MSE Score: 0.003571285\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 44\n",
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      "\n",
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      "Validation Reconstruction Score: 35.17827\n",
      "Validation MSE Score: 0.0037733857\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 46\n",
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      "Validation Reconstruction Score: 35.085026\n",
      "Validation MSE Score: 0.0037786076\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 47\n",
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      "\n",
      "Epoch: 48\n",
      "Total Loss: 0.45311810416834697\n",
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      "\n",
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      "\n",
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      "\n",
      "Epoch: 51\n",
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      "\n",
      "Epoch: 52\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
      "Epoch: 54\n",
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      "Validation MSE Score: 0.0036871347\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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      "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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      "\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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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     "text": [
      "Validation Reconstruction Score: 34.7727\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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      "\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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      "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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      "\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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      "Reconstruction Loss: 0.41262157559394835\n",
      "KL Loss: 0.001306753754449476\n",
      "Regression Loss: 0.004446991928853094\n",
      "Trainig Reconstruction Score: 33.508873\n",
      "Training MSE Score: 0.00447262\n",
      "Validation Reconstruction Score: 34.425438\n",
      "Validation MSE Score: 0.0033425617\n",
      "\n",
      "Epoch: 195\n",
      "Total Loss: 0.41831673426287513\n",
      "Reconstruction Loss: 0.4125370502471924\n",
      "KL Loss: 0.001307846956686782\n",
      "Regression Loss: 0.004471837292658165\n",
      "Trainig Reconstruction Score: 33.514435\n",
      "Training MSE Score: 0.004451229\n",
      "Validation Reconstruction Score: 34.419743\n",
      "Validation MSE Score: 0.0033581746\n",
      "\n",
      "Epoch: 196\n",
      "Total Loss: 0.4181220007794244\n",
      "Reconstruction Loss: 0.4123529008456639\n",
      "KL Loss: 0.00130747979523481\n",
      "Regression Loss: 0.004461620141851849\n",
      "Trainig Reconstruction Score: 33.516396\n",
      "Training MSE Score: 0.004427843\n",
      "Validation Reconstruction Score: 34.43361\n",
      "Validation MSE Score: 0.0033741253\n",
      "\n",
      "Epoch: 197\n",
      "Total Loss: 0.41795233062335424\n",
      "Reconstruction Loss: 0.41219303224767956\n",
      "KL Loss: 0.0013054759923501738\n",
      "Regression Loss: 0.004453821199214352\n",
      "Trainig Reconstruction Score: 33.511772\n",
      "Training MSE Score: 0.004420542\n",
      "Validation Reconstruction Score: 34.434315\n",
      "Validation MSE Score: 0.0033850996\n",
      "\n",
      "Epoch: 198\n",
      "Total Loss: 0.4181304088660649\n",
      "Reconstruction Loss: 0.4123551709311349\n",
      "KL Loss: 0.001303115490424846\n",
      "Regression Loss: 0.004472123246107782\n",
      "Trainig Reconstruction Score: 33.51223\n",
      "Training MSE Score: 0.0044238106\n",
      "Validation Reconstruction Score: 34.41262\n",
      "Validation MSE Score: 0.0033752236\n",
      "\n",
      "Epoch: 199\n",
      "Total Loss: 0.4181665275778089\n",
      "Reconstruction Loss: 0.41239263457911357\n",
      "KL Loss: 0.0013039774677184011\n",
      "Regression Loss: 0.004469915545944656\n",
      "Trainig Reconstruction Score: 33.509644\n",
      "Training MSE Score: 0.0044369274\n",
      "Validation Reconstruction Score: 34.444427\n",
      "Validation MSE Score: 0.0034352732\n"
     ]
    }
   ],
   "source": [
    "# VAE conv kernel size = 7 z_dim=2\n",
    "BATCH_SIZE = 10\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)\n",
    "train_loader_score = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=0)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)\n",
    "\n",
    "#instantiate your model here:\n",
    "model = VAE_REG(kernel_size=7, z_dim=2)\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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test L2 Score: 34.203804\n",
      "Test MSE Score: 0.0049356245\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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