truss_5x5_zdim2_noregression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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: 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: 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: setuptools in /home/u0579755/env_dir/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.8.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/u0579755/env_dir/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib) (1.12.0)\n",
      "\u001b[33mYou are using pip version 19.0.3, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: scikit-image in /home/u0579755/env_dir/lib/python3.6/site-packages (0.15.0)\n",
      "Requirement already satisfied: imageio>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.5.0)\n",
      "Requirement already satisfied: pillow>=4.3.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (5.4.1)\n",
      "Requirement already satisfied: networkx>=2.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (2.3)\n",
      "Requirement already satisfied: PyWavelets>=0.4.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.0.3)\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (3.0.2)\n",
      "Requirement already satisfied: scipy>=0.17.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from scikit-image) (1.2.1)\n",
      "Requirement already satisfied: numpy in /home/u0579755/env_dir/lib/python3.6/site-packages (from imageio>=2.0.1->scikit-image) (1.16.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /home/u0579755/env_dir/lib/python3.6/site-packages (from networkx>=2.0->scikit-image) (4.3.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.7.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/u0579755/env_dir/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.3.1)\n",
      "Requirement already satisfied: 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": 82,
   "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": 83,
   "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": 84,
   "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": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU already assigned before: 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": 86,
   "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": 87,
   "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": 88,
   "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": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open zip of dataset\n",
    "open_zip('trussData.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 91,
   "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": 92,
   "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",
    "        for image, attribute, name in data_loader:\n",
    "            image = image.cuda()\n",
    "            recon, mu, logvar = 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",
    "    \n",
    "    return np.mean(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VAE without Regression - The reconstruction and regression are done from the\n",
    "# latent variables calculated from the reparametrization (distribution)\n",
    "class VAE(torch.nn.Module):\n",
    "    def __init__(self, kernel_size, z_dim):\n",
    "        super(VAE, 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",
    "            \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",
    "        return x, mu, logvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function for this task, combines reconstruction and KL for the image task\n",
    "def loss_function(reconstruction, image, mu, logvar, attributes, 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",
    "    return reconstruction_loss, kl_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Training!\n",
      "\n",
      "Epoch: 0\n",
      "Total Loss: 0.6874038432325635\n",
      "Reconstruction Loss: 0.6871936976909637\n",
      "KL Loss: 0.00021014512505124523\n",
      "Trainig Reconstruction Score: 44.30928\n",
      "Validation Reconstruction Score: 44.483887\n",
      "\n",
      "Epoch: 1\n",
      "Total Loss: 0.6512689547879356\n",
      "Reconstruction Loss: 0.6505446510655539\n",
      "KL Loss: 0.0007243017898872495\n",
      "Trainig Reconstruction Score: 43.5685\n",
      "Validation Reconstruction Score: 43.80905\n",
      "\n",
      "Epoch: 2\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "New Best Model Saved!\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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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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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainig Reconstruction Score: 34.68868\n",
      "Validation Reconstruction Score: 35.07768\n",
      "\n",
      "Epoch: 42\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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      "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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      "\n",
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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",
      "Epoch: 147\n",
      "Total Loss: 0.42159654114927564\n",
      "Reconstruction Loss: 0.42032293123858316\n",
      "KL Loss: 0.0012736106607397753\n",
      "Trainig Reconstruction Score: 33.8841\n",
      "Validation Reconstruction Score: 34.503483\n",
      "\n",
      "Epoch: 148\n",
      "Total Loss: 0.4216680718319757\n",
      "Reconstruction Loss: 0.4204014735562461\n",
      "KL Loss: 0.0012665966941442872\n",
      "Trainig Reconstruction Score: 33.881462\n",
      "Validation Reconstruction Score: 34.560226\n",
      "\n",
      "Epoch: 149\n",
      "Total Loss: 0.4215712721858706\n",
      "Reconstruction Loss: 0.42030530273914335\n",
      "KL Loss: 0.0012659693685626344\n",
      "Trainig Reconstruction Score: 33.904236\n",
      "Validation Reconstruction Score: 34.55845\n",
      "\n",
      "Epoch: 150\n",
      "Total Loss: 0.41869596583502633\n",
      "Reconstruction Loss: 0.41742617658206393\n",
      "KL Loss: 0.0012697885261981614\n",
      "Trainig Reconstruction Score: 33.70552\n",
      "Validation Reconstruction Score: 34.37945\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 151\n",
      "Total Loss: 0.41757042961461205\n",
      "Reconstruction Loss: 0.4163013334785189\n",
      "KL Loss: 0.001269095271293606\n",
      "Trainig Reconstruction Score: 33.683136\n",
      "Validation Reconstruction Score: 34.34692\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 152\n",
      "Total Loss: 0.41718255111149377\n",
      "Reconstruction Loss: 0.41591532868998393\n",
      "KL Loss: 0.0012672223965637386\n",
      "Trainig Reconstruction Score: 33.673443\n",
      "Validation Reconstruction Score: 34.336964\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 153\n",
      "Total Loss: 0.4170267790555954\n",
      "Reconstruction Loss: 0.4157597405569894\n",
      "KL Loss: 0.0012670374891188528\n",
      "Trainig Reconstruction Score: 33.668003\n",
      "Validation Reconstruction Score: 34.36128\n",
      "\n",
      "Epoch: 154\n",
      "Total Loss: 0.41665459402969907\n",
      "Reconstruction Loss: 0.4153893142938614\n",
      "KL Loss: 0.0012652795961392777\n",
      "Trainig Reconstruction Score: 33.662476\n",
      "Validation Reconstruction Score: 34.35494\n",
      "\n",
      "Epoch: 155\n",
      "Total Loss: 0.41679574932370866\n",
      "Reconstruction Loss: 0.41552928941590445\n",
      "KL Loss: 0.0012664603584978198\n",
      "Trainig Reconstruction Score: 33.658867\n",
      "Validation Reconstruction Score: 34.347572\n",
      "\n",
      "Epoch: 156\n",
      "Total Loss: 0.4165877227272306\n",
      "Reconstruction Loss: 0.41532322210924966\n",
      "KL Loss: 0.0012644995103723237\n",
      "Trainig Reconstruction Score: 33.665047\n",
      "Validation Reconstruction Score: 34.359077\n",
      "\n",
      "Epoch: 157\n",
      "Total Loss: 0.41655517177922385\n",
      "Reconstruction Loss: 0.41529251124177663\n",
      "KL Loss: 0.0012626600002736917\n",
      "Trainig Reconstruction Score: 33.658035\n",
      "Validation Reconstruction Score: 34.352997\n",
      "\n",
      "Epoch: 158\n",
      "Total Loss: 0.4165208795240947\n",
      "Reconstruction Loss: 0.4152589018855776\n",
      "KL Loss: 0.0012619767604129656\n",
      "Trainig Reconstruction Score: 33.651936\n",
      "Validation Reconstruction Score: 34.333527\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 159\n",
      "Total Loss: 0.41639262693268914\n",
      "Reconstruction Loss: 0.41513117849826814\n",
      "KL Loss: 0.0012614487736885038\n",
      "Trainig Reconstruction Score: 33.649887\n",
      "Validation Reconstruction Score: 34.338272\n",
      "\n",
      "Epoch: 160\n",
      "Total Loss: 0.41658858231135776\n",
      "Reconstruction Loss: 0.41532877811363766\n",
      "KL Loss: 0.0012598069384694099\n",
      "Trainig Reconstruction Score: 33.648033\n",
      "Validation Reconstruction Score: 34.31772\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 161\n",
      "Total Loss: 0.41641834250518256\n",
      "Reconstruction Loss: 0.4151572116783687\n",
      "KL Loss: 0.0012611302871456636\n",
      "Trainig Reconstruction Score: 33.654663\n",
      "Validation Reconstruction Score: 34.345795\n",
      "\n",
      "Epoch: 162\n",
      "Total Loss: 0.41633980742522647\n",
      "Reconstruction Loss: 0.41507980695792607\n",
      "KL Loss: 0.0012600001363482858\n",
      "Trainig Reconstruction Score: 33.644768\n",
      "Validation Reconstruction Score: 34.342896\n",
      "\n",
      "Epoch: 163\n",
      "Total Loss: 0.41625327851091115\n",
      "Reconstruction Loss: 0.41499403502259935\n",
      "KL Loss: 0.0012592426534476025\n",
      "Trainig Reconstruction Score: 33.640884\n",
      "Validation Reconstruction Score: 34.328564\n",
      "\n",
      "Epoch: 164\n",
      "Total Loss: 0.4164402093206133\n",
      "Reconstruction Loss: 0.41517948125089915\n",
      "KL Loss: 0.0012607274360821716\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainig Reconstruction Score: 33.64325\n",
      "Validation Reconstruction Score: 34.341698\n",
      "\n",
      "Epoch: 165\n",
      "Total Loss: 0.4163514128753117\n",
      "Reconstruction Loss: 0.4150919122355325\n",
      "KL Loss: 0.0012594994007875877\n",
      "Trainig Reconstruction Score: 33.64573\n",
      "Validation Reconstruction Score: 34.344383\n",
      "\n",
      "Epoch: 166\n",
      "Total Loss: 0.41624794006347654\n",
      "Reconstruction Loss: 0.4149905502796173\n",
      "KL Loss: 0.0012573894321186733\n",
      "Trainig Reconstruction Score: 33.63831\n",
      "Validation Reconstruction Score: 34.33312\n",
      "\n",
      "Epoch: 167\n",
      "Total Loss: 0.4164642725672041\n",
      "Reconstruction Loss: 0.41520416608878546\n",
      "KL Loss: 0.0012601079502409058\n",
      "Trainig Reconstruction Score: 33.643784\n",
      "Validation Reconstruction Score: 34.349586\n",
      "\n",
      "Epoch: 168\n",
      "Total Loss: 0.4161712131329945\n",
      "Reconstruction Loss: 0.414911482163838\n",
      "KL Loss: 0.0012597309425473214\n",
      "Trainig Reconstruction Score: 33.632298\n",
      "Validation Reconstruction Score: 34.322865\n",
      "\n",
      "Epoch: 169\n",
      "Total Loss: 0.4160821752888816\n",
      "Reconstruction Loss: 0.4148237773350307\n",
      "KL Loss: 0.0012583981733769178\n",
      "Trainig Reconstruction Score: 33.645245\n",
      "Validation Reconstruction Score: 34.355915\n",
      "\n",
      "Epoch: 170\n",
      "Total Loss: 0.41632908710411615\n",
      "Reconstruction Loss: 0.4150699232305799\n",
      "KL Loss: 0.001259162910615227\n",
      "Trainig Reconstruction Score: 33.638\n",
      "Validation Reconstruction Score: 34.340378\n",
      "\n",
      "Epoch: 171\n",
      "Total Loss: 0.41625047240938456\n",
      "Reconstruction Loss: 0.4149902616228376\n",
      "KL Loss: 0.0012602100248581597\n",
      "Trainig Reconstruction Score: 33.628677\n",
      "Validation Reconstruction Score: 34.328156\n",
      "\n",
      "Epoch: 172\n",
      "Total Loss: 0.4161231845617294\n",
      "Reconstruction Loss: 0.41486372479370665\n",
      "KL Loss: 0.0012594598960796637\n",
      "Trainig Reconstruction Score: 33.640327\n",
      "Validation Reconstruction Score: 34.345997\n",
      "\n",
      "Epoch: 173\n",
      "Total Loss: 0.4161415845155716\n",
      "Reconstruction Loss: 0.4148846277168819\n",
      "KL Loss: 0.0012569564311499042\n",
      "Trainig Reconstruction Score: 33.63164\n",
      "Validation Reconstruction Score: 34.321068\n",
      "\n",
      "Epoch: 174\n",
      "Total Loss: 0.4161918099437441\n",
      "Reconstruction Loss: 0.4149320321423667\n",
      "KL Loss: 0.001259779723893319\n",
      "Trainig Reconstruction Score: 33.647385\n",
      "Validation Reconstruction Score: 34.36218\n",
      "\n",
      "Epoch: 175\n",
      "Total Loss: 0.4158504179545811\n",
      "Reconstruction Loss: 0.41459218263626096\n",
      "KL Loss: 0.0012582349798841669\n",
      "Trainig Reconstruction Score: 33.630325\n",
      "Validation Reconstruction Score: 34.347004\n",
      "\n",
      "Epoch: 176\n",
      "Total Loss: 0.4159728122609002\n",
      "Reconstruction Loss: 0.41471463228975025\n",
      "KL Loss: 0.0012581807461434177\n",
      "Trainig Reconstruction Score: 33.62689\n",
      "Validation Reconstruction Score: 34.342445\n",
      "\n",
      "Epoch: 177\n",
      "Total Loss: 0.4159091114997864\n",
      "Reconstruction Loss: 0.4146525263786316\n",
      "KL Loss: 0.0012565859892804709\n",
      "Trainig Reconstruction Score: 33.633923\n",
      "Validation Reconstruction Score: 34.339108\n",
      "\n",
      "Epoch: 178\n",
      "Total Loss: 0.41606635068144115\n",
      "Reconstruction Loss: 0.4148076866354261\n",
      "KL Loss: 0.0012586634123830922\n",
      "Trainig Reconstruction Score: 33.627686\n",
      "Validation Reconstruction Score: 34.342545\n",
      "\n",
      "Epoch: 179\n",
      "Total Loss: 0.41589513165610176\n",
      "Reconstruction Loss: 0.41463584133556913\n",
      "KL Loss: 0.001259291369933635\n",
      "Trainig Reconstruction Score: 33.623863\n",
      "Validation Reconstruction Score: 34.33085\n",
      "\n",
      "Epoch: 180\n",
      "Total Loss: 0.4158178938286645\n",
      "Reconstruction Loss: 0.4145602937255587\n",
      "KL Loss: 0.0012575983718436743\n",
      "Trainig Reconstruction Score: 33.63294\n",
      "Validation Reconstruction Score: 34.359005\n",
      "\n",
      "Epoch: 181\n",
      "Total Loss: 0.41585551628044676\n",
      "Reconstruction Loss: 0.41459665937083107\n",
      "KL Loss: 0.0012588566510073308\n",
      "Trainig Reconstruction Score: 33.620495\n",
      "Validation Reconstruction Score: 34.328842\n",
      "\n",
      "Epoch: 182\n",
      "Total Loss: 0.4157844705241067\n",
      "Reconstruction Loss: 0.4145260325499943\n",
      "KL Loss: 0.0012584382618245269\n",
      "Trainig Reconstruction Score: 33.616745\n",
      "Validation Reconstruction Score: 34.315224\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 183\n",
      "Total Loss: 0.4157893730061395\n",
      "Reconstruction Loss: 0.41453053738389695\n",
      "KL Loss: 0.0012588359465423439\n",
      "Trainig Reconstruction Score: 33.6217\n",
      "Validation Reconstruction Score: 34.340225\n",
      "\n",
      "Epoch: 184\n",
      "Total Loss: 0.41580824809415\n",
      "Reconstruction Loss: 0.4145495797906603\n",
      "KL Loss: 0.0012586689038601305\n",
      "Trainig Reconstruction Score: 33.61466\n",
      "Validation Reconstruction Score: 34.31791\n",
      "\n",
      "Epoch: 185\n",
      "Total Loss: 0.41583479600293294\n",
      "Reconstruction Loss: 0.4145775190421513\n",
      "KL Loss: 0.0012572771087954087\n",
      "Trainig Reconstruction Score: 33.627647\n",
      "Validation Reconstruction Score: 34.34923\n",
      "\n",
      "Epoch: 186\n",
      "Total Loss: 0.41567070058413913\n",
      "Reconstruction Loss: 0.41441238948277065\n",
      "KL Loss: 0.0012583095098047383\n",
      "Trainig Reconstruction Score: 33.621246\n",
      "Validation Reconstruction Score: 34.337112\n",
      "\n",
      "Epoch: 187\n",
      "Total Loss: 0.41558401158877784\n",
      "Reconstruction Loss: 0.41432596274784633\n",
      "KL Loss: 0.0012580488775191562\n",
      "Trainig Reconstruction Score: 33.61398\n",
      "Validation Reconstruction Score: 34.321648\n",
      "\n",
      "Epoch: 188\n",
      "Total Loss: 0.4156629992382867\n",
      "Reconstruction Loss: 0.41440609906400955\n",
      "KL Loss: 0.001256900776310691\n",
      "Trainig Reconstruction Score: 33.61514\n",
      "Validation Reconstruction Score: 34.303837\n",
      "New Best Model Saved!\n",
      "\n",
      "Epoch: 189\n",
      "Total Loss: 0.41568986645766665\n",
      "Reconstruction Loss: 0.41443066000938417\n",
      "KL Loss: 0.0012592071767098137\n",
      "Trainig Reconstruction Score: 33.612846\n",
      "Validation Reconstruction Score: 34.317963\n",
      "\n",
      "Epoch: 190\n",
      "Total Loss: 0.41575056867940086\n",
      "Reconstruction Loss: 0.4144917952162879\n",
      "KL Loss: 0.001258772292307445\n",
      "Trainig Reconstruction Score: 33.613213\n",
      "Validation Reconstruction Score: 34.32051\n",
      "\n",
      "Epoch: 191\n",
      "Total Loss: 0.415542785184724\n",
      "Reconstruction Loss: 0.414285933971405\n",
      "KL Loss: 0.0012568511201867035\n",
      "Trainig Reconstruction Score: 33.6175\n",
      "Validation Reconstruction Score: 34.346485\n",
      "\n",
      "Epoch: 192\n",
      "Total Loss: 0.4157958903482982\n",
      "Reconstruction Loss: 0.41453806332179477\n",
      "KL Loss: 0.0012578261966284896\n",
      "Trainig Reconstruction Score: 33.612392\n",
      "Validation Reconstruction Score: 34.34195\n",
      "\n",
      "Epoch: 193\n",
      "Total Loss: 0.4156838838543211\n",
      "Reconstruction Loss: 0.4144245258399418\n",
      "KL Loss: 0.001259359294947769\n",
      "Trainig Reconstruction Score: 33.608208\n",
      "Validation Reconstruction Score: 34.340004\n",
      "\n",
      "Epoch: 194\n",
      "Total Loss: 0.41555105234895434\n",
      "Reconstruction Loss: 0.4142941977296557\n",
      "KL Loss: 0.0012568566781867828\n",
      "Trainig Reconstruction Score: 33.60745\n",
      "Validation Reconstruction Score: 34.33156\n",
      "\n",
      "Epoch: 195\n",
      "Total Loss: 0.4154659982238497\n",
      "Reconstruction Loss: 0.41420937521117074\n",
      "KL Loss: 0.0012566232987280402\n",
      "Trainig Reconstruction Score: 33.604748\n",
      "Validation Reconstruction Score: 34.332966\n",
      "\n",
      "Epoch: 196\n",
      "Total Loss: 0.41567043789795466\n",
      "Reconstruction Loss: 0.41441295402390615\n",
      "KL Loss: 0.001257483659511698\n",
      "Trainig Reconstruction Score: 33.606915\n",
      "Validation Reconstruction Score: 34.312984\n",
      "\n",
      "Epoch: 197\n",
      "Total Loss: 0.41582789633955275\n",
      "Reconstruction Loss: 0.41457029368196213\n",
      "KL Loss: 0.001257603068370372\n",
      "Trainig Reconstruction Score: 33.6112\n",
      "Validation Reconstruction Score: 34.358185\n",
      "\n",
      "Epoch: 198\n",
      "Total Loss: 0.4153129722390856\n",
      "Reconstruction Loss: 0.4140564475740705\n",
      "KL Loss: 0.0012565225712023675\n",
      "Trainig Reconstruction Score: 33.60688\n",
      "Validation Reconstruction Score: 34.340397\n",
      "\n",
      "Epoch: 199\n",
      "Total Loss: 0.41546484700271064\n",
      "Reconstruction Loss: 0.41420623149190633\n",
      "KL Loss: 0.001258615472553564\n",
      "Trainig Reconstruction Score: 33.608696\n",
      "Validation Reconstruction Score: 34.338013\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(kernel_size=5, 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",
    "val_recon_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",
    "    for image, attribute, name in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        image = image.cuda()\n",
    "        attribute = attribute.cuda()\n",
    "        recon, mu, logvar = model(image)\n",
    "        reconstruction_loss, kl_loss = loss_function(recon, image, mu, logvar, attribute, BATCH_SIZE)\n",
    "        loss = reconstruction_loss + kl_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",
    "        \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",
    "    \n",
    "    # Get training data accuracy\n",
    "    train_recon_score = get_score_model(model, train_loader_score)\n",
    "    print('Trainig Reconstruction Score: ' + str(train_recon_score))\n",
    "    train_recon_scores.append(train_recon_score)\n",
    "    \n",
    "    # Get validation data accuracy\n",
    "    val_recon_score = get_score_model(model, val_loader)\n",
    "    print('Validation Reconstruction Score: ' + str(val_recon_score))\n",
    "    val_recon_scores.append(val_recon_score)\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": 97,
   "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": 99,
   "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 = 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": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test L2 Score: 34.388287\n"
     ]
    }
   ],
   "source": [
    "test_recon_score = get_score_model(best_model, test_loader)\n",
    "print('Test L2 Score: ' + str(test_recon_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gets the twod representation of teh data\n",
    "def get_2D_representation(model, data_loader):\n",
    "    model.eval()\n",
    "    all_frame = pd.DataFrame()\n",
    "    with torch.no_grad():\n",
    "        for image, attribute, name in data_loader:\n",
    "            image = image.cuda()\n",
    "            two_d = model.representation(image)\n",
    "            two_d = two_d.cpu().numpy()\n",
    "            frame = pd.DataFrame(data=two_d, columns=['x', 'y'])\n",
    "            frame['max_stress'] = attribute.cpu().numpy()[0] \n",
    "            all_frame = all_frame.append(frame)\n",
    "    return all_frame\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "frame = get_2D_representation(best_model, train_loader_score)\n",
    "frame = frame.sort_values('max_stress')\n",
    "minimum = frame.max_stress.min()\n",
    "maximum = frame.max_stress.max()\n",
    "fig, ax = plt.subplots(dpi=200)\n",
    "ax.axes.get_xaxis().set_visible(False)\n",
    "ax.axes.get_yaxis().set_visible(False)\n",
    "blue_cmap = cm.get_cmap('Blues')\n",
    "\n",
    "plt.scatter(frame.x, frame.y, c=frame.max_stress, s=30, cmap=blue_cmap, edgecolors='k')\n",
    "\n",
    "legend_elements = [Line2D([0], [0], marker='o', color='w', label='Minimum', markeredgewidth=1, markeredgecolor='k', markerfacecolor=blue_cmap(0), markersize=6),\n",
    "                   Line2D([0], [0], marker='o', color='w', label='Maximum', markeredgewidth=1, markeredgecolor='k', markerfacecolor=blue_cmap(maximum), markersize=6)]\n",
    "plt.legend(handles=legend_elements)\n",
    "\n",
    "plt.savefig('twodrep_noregression.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}