full_collection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/site-packages\n",
      "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/site-packages (from pandas)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/site-packages (from pandas)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/site-packages (from pandas)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.5.0->pandas)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: pydicom in /usr/local/lib/python3.6/site-packages\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: seaborn in /usr/local/lib/python3.6/site-packages\n",
      "Requirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /usr/local/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /usr/local/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn)\n",
      "Requirement already satisfied: pytz in /usr/local/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn)\n",
      "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: memory_profiler in /usr/local/lib/python3.6/site-packages\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.6/site-packages (from memory_profiler)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Load modules, packages, and functions\n",
    "! pip install pandas\n",
    "! pip install pydicom\n",
    "! pip install seaborn\n",
    "! pip install memory_profiler\n",
    "%load_ext memory_profiler\n",
    "\n",
    "import glob, pylab, pandas as pd\n",
    "import pydicom, numpy as np\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from skimage.transform import resize\n",
    "from skimage.exposure import equalize_hist\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image, display, clear_output\n",
    "import numpy as np\n",
    "%matplotlib nbagg\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(sns.dark_palette(\"purple\"))\n",
    "\n",
    "import torch\n",
    "cuda = torch.cuda.is_available()\n",
    "\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision.transforms import ToTensor\n",
    "from functools import reduce\n",
    "\n",
    "import torch.nn as nn\n",
    "from torch.nn.functional import softplus\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from itertools import cycle\n",
    "import operator\n",
    "from torch.nn import Linear, GRU, Conv2d, Dropout, Dropout2d, MaxPool2d, BatchNorm1d, BatchNorm2d, ReLU, ELU, ConvTranspose2d, MaxUnpool2d, Softmax, Sigmoid, Softsign\n",
    "from torch.nn.functional import relu, elu, relu6, sigmoid, tanh, softmax, dropout, dropout2d, interpolate\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logging( i, print_every, no_samples, tic, elapsed_time):\n",
    "    \"\"\"\n",
    "    Function for logging: timing and counting\n",
    "    Inputs:\n",
    "    - i:            Iteration i \n",
    "    - print_every:  Print logging in the interval of print_every\n",
    "    - no_samples:   Number of samples\n",
    "    - tic:          Start timer for last time\n",
    "    - elapsed_time: Amount of time elapsed \n",
    "    \"\"\"\n",
    "    if not i % print_every:\n",
    "        toc = time.clock()\n",
    "        period_time = toc - tic;\n",
    "        if i > 0:\n",
    "            elapsed_time = (elapsed_time + period_time)\n",
    "            mean_period_time = elapsed_time/ ((i)/print_every)\n",
    "            minutes = round(mean_period_time*( (No_samples_train-i)//print_every)//60)\n",
    "            seconds = round(mean_period_time*( (No_samples_train-i)/print_every)%60)\n",
    "            print(\"Data loaded:\", i,\"/\",No_samples_train,  \"    Remaining time: \", minutes,\":\", seconds)\n",
    "        tic = time.clock(); \n",
    "    else:\n",
    "        tic = -1\n",
    "        elapsed_time = -1\n",
    "    return tic, elapsed_time\n",
    "\n",
    "\n",
    "def uniform_stratified_sampler(labels, n=None):\n",
    "    \"\"\"\n",
    "    Stratified sampler that distributes labels uniformly by\n",
    "    sampling at most n data points per class\n",
    "    \"\"\"\n",
    "    from functools import reduce\n",
    "    # Only choose digits in n_labels\n",
    "    (indices,) = np.where(reduce(lambda x, y: x | y, [labels.numpy() == i for i in classes]))\n",
    "\n",
    "    # Ensure uniform distribution of labels\n",
    "    np.random.shuffle(indices)\n",
    "    indices = np.hstack([list(filter(lambda idx: labels[idx] == i, indices))[:n] for i in classes])\n",
    "\n",
    "    indices = torch.from_numpy(indices)\n",
    "    sampler = SubsetRandomSampler(indices)\n",
    "    return sampler\n",
    "\n",
    "\n",
    "def construct_DataLoader( Target, Image, batch_size, labels_per_class=None):\n",
    "    \"\"\"\n",
    "    Contruct DataLoader with tensors\n",
    "    Inputs:\n",
    "    - Target:            Iteration i \n",
    "    - Image:  Print logging in the interval of print_every\n",
    "    - batch_size\n",
    "    \"\"\"\n",
    "    # Convert to Tensor\n",
    "    Target = torch.Tensor(Target)\n",
    "    Image = torch.Tensor(Image)\n",
    "    Image = Image.unsqueeze(1)\n",
    "\n",
    "    # Construct DataLoader\n",
    "    loader = TensorDataset(Image, Target)\n",
    "    if labels_per_class==None:\n",
    "        dataLoader = DataLoader(loader, batch_size=batch_size, shuffle = True)\n",
    "    else:    \n",
    "        dataLoader = DataLoader(loader, batch_size=batch_size,\n",
    "                     sampler=uniform_stratified_sampler(Target, labels_per_class))\n",
    "    return dataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patientId    00436515-870c-4b36-a041-de91049b9ab4\n",
      "x                                             264\n",
      "y                                             152\n",
      "width                                         213\n",
      "height                                        379\n",
      "Target                                          1\n",
      "Name: 4, dtype: object\n"
     ]
    }
   ],
   "source": [
    "######### Defining the data set #########\n",
    "\n",
    "KAGGLE = False\n",
    " \n",
    "# Display label format\n",
    "if KAGGLE:\n",
    "    df = pd.read_csv('../input/stage_2_detailed_class_info.csv')\n",
    "    df.rename(columns={'class': 'Target'}, inplace=True)\n",
    "    mapping = {'Normal': 0, 'Lung Opacity': 1, 'No Lung Opacity / Not Normal': 2}\n",
    "    df = df.replace({'Target': mapping})\n",
    "    df = df[ df['Target']!=2 ]\n",
    "    df = df.reset_index(drop=True)\n",
    "else:\n",
    "    df = pd.read_csv('data/stage_1_train_labels.csv')\n",
    "\n",
    "# Defining the size of the data set\n",
    "classes = [0,1]          # ['Normal , 'Lung Opacity']  \n",
    "batch_size       = 64\n",
    "labels_per_class = 32    # Specify how many labelled examples we want per class\n",
    "No_samples_train_labelled = labels_per_class*len(classes)\n",
    "No_samples_train = 64\n",
    "No_samples_test  = 64\n",
    "IMG_SIZE         = 32\n",
    "img_dimension = [IMG_SIZE,IMG_SIZE]\n",
    "\n",
    "\n",
    "# Test connection to data set\n",
    "No = 4\n",
    "patientId = df['patientId'][No]\n",
    "if KAGGLE:\n",
    "    dcm_file = '../input/stage_2_train_images/%s.dcm' % patientId\n",
    "else:\n",
    "    dcm_file = 'data/stage_1_train_images/%s.dcm' % patientId\n",
    "dcm_data = pydicom.read_file(dcm_file)\n",
    "print(df.iloc[No])\n",
    "\n",
    "# Get only unique entrances from the provided data (some patients occur multiple times)\n",
    "unq, idx = np.unique(df['patientId'], return_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "3f518499954d2ad60289c06138d796a17a6f5aee",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading training images: 0 / 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded: 10 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 20 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 30 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 40 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 50 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 60 / 64     Remaining time:  0 : 0\n",
      "Train data loaded: 64\n",
      " \n",
      "Data loaded: 10 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 20 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 30 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 40 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 50 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 60 / 64     Remaining time:  0 : 0\n",
      "Test data loaded: 64\n",
      " \n",
      "Data loaded: 10 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 20 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 30 / 64     Remaining time:  0 : 1\n",
      "Data loaded: 40 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 50 / 64     Remaining time:  0 : 0\n",
      "Data loaded: 60 / 64     Remaining time:  0 : 0\n",
      "Labelled data loaded: 87\n"
     ]
    }
   ],
   "source": [
    "######### Load data into DataLoader's #########\n",
    "# Load unlabelled training data\n",
    "# Load labelled test data\n",
    "# Load labelled training data\n",
    "\n",
    "print_every = 10\n",
    "Do_img_eq = True\n",
    "\n",
    "######### Loading UNLABELED TRAINING data #########\n",
    "Target = []; Image = []\n",
    "tic = time.clock(); elapsed_time = 0\n",
    "\n",
    "print(\"Loading training images: 0 /\", No_samples_train)\n",
    "for i in range(0,No_samples_train):\n",
    "    Target.append(df.Target[idx[i]]) # Get label  \n",
    "    patientId = df['patientId'][idx[i]] # Get patient id from the idx \n",
    "    if KAGGLE:\n",
    "        dcm_file = '../input/stage_2_train_images/%s.dcm' % patientId # find the image-file corresponding to the patient id\n",
    "    else:\n",
    "        dcm_file = 'data/stage_1_train_images/%s.dcm' % patientId # find the image-file corresponding to the patient id\n",
    "    dcm_data = pydicom.read_file(dcm_file) # Load the image \n",
    "    Image.append(resize(dcm_data.pixel_array, output_shape=img_dimension, mode='reflect'))#, anti_aliasing=True)) # resize image\n",
    "    if Do_img_eq:\n",
    "        Image[-1] = equalize_hist(Image[-1])\n",
    "    \n",
    "    # Logging: counting and time remaining\n",
    "    val1, val2 = logging(i, print_every, No_samples_train, tic, elapsed_time)\n",
    "    if not val1 == -1:\n",
    "        tic = val1\n",
    "        elapsed_time = val2    \n",
    "    i = i + 1\n",
    "    \n",
    "print(\"Train data loaded:\", i)\n",
    "train_loader = construct_DataLoader( Target, Image, batch_size)\n",
    "print(' ')\n",
    "\n",
    "\n",
    "######### Loading TEST data #########\n",
    "Target = []; Image = []\n",
    "tic = time.clock(); elapsed_time = 0\n",
    "for i in range(0,No_samples_test):\n",
    "    Target.append(df.Target[idx[No_samples_train+i]]) # Get label  \n",
    "    patientId = df['patientId'][idx[No_samples_train+i]] # Get patient id from the idx \n",
    "    if KAGGLE:\n",
    "        dcm_file = '../input/stage_2_train_images/%s.dcm' % patientId # find the image-file corresponding to the patient id\n",
    "    else:\n",
    "        dcm_file = 'data/stage_1_train_images/%s.dcm' % patientId # find the image-file corresponding to the patient id\n",
    "    dcm_data = pydicom.read_file(dcm_file) # Load the image \n",
    "    Image.append(resize(dcm_data.pixel_array, output_shape=img_dimension, mode='reflect'))#, anti_aliasing=True)) # resize image\n",
    "    if Do_img_eq:\n",
    "        Image[-1] = equalize_hist(Image[-1])\n",
    "        \n",
    "    # Logging: counting and time remaining\n",
    "    val1, val2 = logging(i, print_every, No_samples_test, tic, elapsed_time)\n",
    "    if not val1 == -1:\n",
    "        tic = val1\n",
    "        elapsed_time = val2\n",
    "    i = i + 1\n",
    "    \n",
    "print(\"Test data loaded:\", i)\n",
    "test_loader = construct_DataLoader( Target, Image, batch_size)\n",
    "print(' ')\n",
    "\n",
    "\n",
    "######### Loading LABELED TRAINING data #########\n",
    "Target = []; Image = []\n",
    "tic = time.clock(); elapsed_time = 0\n",
    "count_unlabelled = 0; count_labelled = 0\n",
    "count_old = 0;\n",
    "i = 0\n",
    "while count_labelled<labels_per_class or count_unlabelled<labels_per_class:\n",
    "    Target.append(df.Target[idx[No_samples_test+No_samples_train+i]]) # Get label  \n",
    "\n",
    "    if Target[i]==1:\n",
    "        count_labelled = count_labelled + 1\n",
    "    else:\n",
    "        count_unlabelled = count_unlabelled + 1\n",
    "        \n",
    "    patientId = df['patientId'][idx[No_samples_test+No_samples_train+i]] # Get patient id from the idx \n",
    "    if KAGGLE:\n",
    "        dcm_file = '../input/stage_2_train_images/%s.dcm' % patientId # find the image-file corresponding to the patient id\n",
    "    else:\n",
    "        dcm_file = 'data/stage_1_train_images/%s.dcm' % patientId # find the image-file corresponding to the patient id\n",
    "    dcm_data = pydicom.read_file(dcm_file) # Load the image \n",
    "    Image.append(resize(dcm_data.pixel_array, output_shape=img_dimension, mode='reflect'))#, anti_aliasing=True)) # resize image\n",
    "    if Do_img_eq:\n",
    "        Image[-1] = equalize_hist(Image[-1])\n",
    "\n",
    "    # Logging: counting and time remaining\n",
    "    count = min(count_labelled, count_unlabelled)\n",
    "    if (count > count_old):\n",
    "        count_old = min(count_labelled, count_unlabelled)\n",
    "        val1, val2 = logging(2*count, print_every, No_samples_train_labelled, tic, elapsed_time)\n",
    "        if not val1 == -1:\n",
    "            tic = val1\n",
    "            elapsed_time = val2  \n",
    "    i = i + 1\n",
    "\n",
    "print(\"Labelled data loaded:\", i)\n",
    "train_loader_labelled = construct_DataLoader( Target, Image, batch_size, labels_per_class)\n",
    "\n",
    "del df, idx, unq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "586fcd78be4c8ec2b945248d87601cf271a9ee42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show an example image \n",
    "im = Image[5]\n",
    "pylab.imshow(im, cmap=pylab.cm.gist_gray)\n",
    "pylab.axis('off')\n",
    "\n",
    "if cuda:\n",
    "    torch.cuda.max_memory_cached(device=0)\n",
    "    torch.cuda.memory_allocated(device = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "05778375fb32b8b3a0a2e5b72822f6db8061e7fb",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 1 1\n",
      "CNN_VAE(\n",
      "  (Encoder_conv): ModuleList(\n",
      "    (0): Conv2d(1, 8, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (1): BatchNorm2d(8, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(8, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): BatchNorm2d(32, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): BatchNorm2d(64, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (8): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (Encoder_FC): ModuleList(\n",
      "    (0): Linear(in_features=64, out_features=1000, bias=True)\n",
      "    (1): BatchNorm1d(1000, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (2): Linear(in_features=1000, out_features=200, bias=True)\n",
      "    (3): BatchNorm1d(200, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (Additional_layer): ModuleList(\n",
      "    (0): Linear(in_features=202, out_features=256, bias=True)\n",
      "    (1): BatchNorm1d(256, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (Decoder_FC): ModuleList(\n",
      "    (0): Linear(in_features=130, out_features=200, bias=True)\n",
      "    (1): BatchNorm1d(200, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (2): Linear(in_features=200, out_features=1000, bias=True)\n",
      "    (3): BatchNorm1d(1000, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (4): Linear(in_features=1000, out_features=64, bias=True)\n",
      "    (5): BatchNorm1d(64, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (Decoder_conv): ModuleList(\n",
      "    (0): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (2): ConvTranspose2d(32, 8, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (3): BatchNorm2d(8, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (4): ConvTranspose2d(8, 2, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (5): BatchNorm2d(2, eps=1e-05, momentum=0.2, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (Classifier): ModuleList(\n",
      "    (0): Linear(in_features=200, out_features=1000, bias=True)\n",
      "    (1): BatchNorm1d(1000, eps=0.0001, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (2): Linear(in_features=1000, out_features=200, bias=True)\n",
      "    (3): BatchNorm1d(200, eps=0.0001, momentum=0.2, affine=True, track_running_stats=True)\n",
      "    (4): Linear(in_features=200, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:336: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n"
     ]
    }
   ],
   "source": [
    "## Define size variables\n",
    "print_shapes = False\n",
    "height = IMG_SIZE\n",
    "width = IMG_SIZE\n",
    "channels = 1\n",
    "num_features = height*width*channels\n",
    "\n",
    "# Regulization\n",
    "L2_reg = 1e-6\n",
    "DROPOUT = True\n",
    "do_p = 0.05 # do_p for conv \n",
    "do_p2 = 0.1 # do_p for linear   NB: Classifier dropout is set manuel to 0.3\n",
    "batchnorm_eps = 1e-5\n",
    "batchnorm_momentum = 0.2\n",
    "\n",
    "# Conv Layers\n",
    "conv_out_channels = [8, 32, 64]\n",
    "conv_kernel = [5, 5, 3]\n",
    "conv_padding = [0, 2, 1]\n",
    "conv_stride = [1, 1, 1]\n",
    "\n",
    "# MaxPool Layers\n",
    "pool_kernel = 3\n",
    "pool_padding = 0\n",
    "pool_stride = 3\n",
    "\n",
    "# Fully connected layers\n",
    "lin_layer = [1000, 200]\n",
    "\n",
    "# auxillary parameters\n",
    "aux_layer = [200, 200]\n",
    "aux_variables = 0\n",
    "aux_in = 2 # layer no. where a is included in encoder\n",
    "aux_decoder_layers = [200,200]\n",
    "\n",
    "# classifier parameters\n",
    "classifier_layer = [1000,200]\n",
    "No_classes = len(classes)\n",
    "\n",
    "# No. of layes\n",
    "NUM_CONV = len(conv_out_channels)\n",
    "NUM_LIN = len(lin_layer)\n",
    "NUM_AUX = len(aux_layer)\n",
    "NUM_CLASS = len(classifier_layer)\n",
    "NUM_AUX_DECODER = len(aux_decoder_layers)\n",
    "\n",
    "# Calculating the dimensions \n",
    "def compute_conv_dim(height, width, kernel_size, padding_size, stride_size):\n",
    "    height_new = int((height - kernel_size + 2 * padding_size) / stride_size + 1)\n",
    "    width_new =  int((width  - kernel_size + 2 * padding_size) / stride_size + 1)\n",
    "    return [height_new, width_new]\n",
    "\n",
    "def compute_final_dimension(height, width, last_num_channels, num_layers):\n",
    "    # First conv layer\n",
    "    CNN_height = height\n",
    "    CNN_width = width\n",
    "    for i in range(num_layers):\n",
    "        # conv layer\n",
    "        CNN_height, CNN_width = compute_conv_dim(CNN_height, CNN_width, conv_kernel[i], conv_padding[i], conv_stride[i])\n",
    "        # maxpool layer\n",
    "        CNN_height, CNN_width = compute_conv_dim(CNN_height, CNN_width, pool_kernel, pool_padding, pool_stride)\n",
    "    final_dim = CNN_height * CNN_width * last_num_channels\n",
    "    print(final_dim,CNN_height,CNN_width)\n",
    "    return [final_dim, CNN_height, CNN_width]\n",
    "\n",
    "def normalize(x):\n",
    "    tmp = x-torch.min( torch.min(x,dim = 2, keepdim = True)[0] ,dim = 3, keepdim = True)[0]\n",
    "    if torch.sum(torch.isnan(tmp))>0:\n",
    "        print(\"nan of tmp\",torch.sum(torch.isnan(tmp)))\n",
    "    return tmp/(torch.max( torch.max(tmp,dim = 2, keepdim = True)[0] ,dim = 3, keepdim = True)[0] + 1e-8)  \n",
    "\n",
    "def gaussian_sample(mu,log_var, num_samples, latent_features):    \n",
    "    # Don't propagate gradients through randomness\n",
    "    with torch.no_grad():\n",
    "        batch_size = mu.size(0)\n",
    "        epsilon = torch.randn(batch_size, num_samples, latent_features)\n",
    "            \n",
    "    if cuda:\n",
    "        epsilon = epsilon.cuda()\n",
    "        \n",
    "    sigma = torch.exp(log_var/2)\n",
    "        \n",
    "    # We will need to unsqueeze to turn\n",
    "    # (batch_size, latent_dim) -> (batch_size, 1, latent_dim)\n",
    "    if len(mu.shape) == 2:\n",
    "        z = mu.unsqueeze(1) + epsilon * sigma.unsqueeze(1)\n",
    "    else:\n",
    "        z = mu + epsilon * sigma\n",
    "    return z\n",
    "\n",
    "def output_recon(x):\n",
    "    # Shape of x_mean: [batch_size, num_samples, channel, height, width]\n",
    "    x_mean, x_log_var = torch.chunk(x, 2, dim=2) # the mean and log_var reconstructions from the decoder\n",
    "    \n",
    "    # The original digits are on the scale [0, 1] \n",
    "    x_hat = x_mean[:,1,].unsqueeze(1)\n",
    "    #x_hat = normalize(x_mean[:,1,].unsqueeze(1))# to scale for showing an image\n",
    "    #x_hat = normalize(x_mean)\n",
    "    x_log_var = softplus(x_log_var)\n",
    "    \n",
    "    # Mean over samples\n",
    "    #x_hat = torch.mean(x_hat, dim=1)\n",
    "    x_log_var= torch.mean(x_log_var, dim=1)\n",
    "    x_mean = torch.mean(x_mean,dim=1) # used for the loss\n",
    "    \n",
    "    # Resize x_hat from [batch_size, no_features] to [batch_size, channels, height, width]\n",
    "    x_hat = x_hat.view( batch_size, 1, height, width)\n",
    "    x_log_var = x_log_var.view( batch_size, 1, height, width)\n",
    "    return x_hat, x_log_var, x_mean\n",
    "\n",
    "######## Image has to be: (num, channels, height, width)!!!! #########\n",
    "class CNN_VAE(nn.Module):\n",
    "    def encoder(self,x):\n",
    "        # Convolutional layers of encoder\n",
    "        for i in range(0,len(self.Encoder_conv),3):\n",
    "            x = self.Encoder_conv[i](x) # Convolutional layer\n",
    "            self.layer_size.append(x.shape[-1])\n",
    "            x = self.Encoder_conv[i+1](x) # Batchnorm layer\n",
    "            x = relu(x)\n",
    "            if DROPOUT:\n",
    "                x = dropout2d(x, p=do_p)   \n",
    "            x = self.Encoder_conv[i+2](x) # Maxpool Layer\n",
    "        x = x.view(batch_size, -1) # Prepare x for linear layers\n",
    "        \n",
    "        # Fully connected layers of encoder\n",
    "        for i in range(0,len(self.Encoder_FC),2): \n",
    "            x = self.Encoder_FC[i](x) # Linear layer\n",
    "            x = self.Encoder_FC[i+1](x) # Batchnorm\n",
    "            x = relu(x)\n",
    "            if DROPOUT:\n",
    "                x = dropout(x, p=do_p2)\n",
    "        return x\n",
    "\n",
    "    \n",
    "    def decoder(self,z,y):\n",
    "        x = torch.cat([z,y],dim=-1)\n",
    "        # Fully connected layers of decoder\n",
    "        for i in range(0,len(self.Decoder_FC),2):\n",
    "            x = self.Decoder_FC[i](x)\n",
    "            x = x.permute(0,2,1)\n",
    "            x = self.Decoder_FC[i+1](x)\n",
    "            x = x.permute(0,2,1)\n",
    "            x = relu(x)\n",
    "            x = dropout(x,p= do_p2)\n",
    "        x = x.view(-1, self.Decoder_conv[0].in_channels, self.final_dim[1], self.final_dim[2])\n",
    "        \n",
    "        # Convolutional layers of decoder\n",
    "        curr_layer = len(self.Decoder_conv)//2-1\n",
    "        for i in range(0,len(self.Decoder_conv),2):\n",
    "            x = interpolate(x,size = [self.layer_size[curr_layer],self.layer_size[curr_layer]],\n",
    "                                      mode = 'bilinear', \n",
    "                                      align_corners = False)\n",
    "            curr_layer -=1\n",
    "            x = self.Decoder_conv[i](x) # Convolutional layers\n",
    "            x = self.Decoder_conv[i+1](x) # BatchNorm\n",
    "            x = relu(x)\n",
    "            if DROPOUT:\n",
    "                x = dropout2d(x, p=do_p)\n",
    "        return x.view(batch_size,-1,channels*2,height,width)\n",
    "            \n",
    "    def encoder_aux(self,a):\n",
    "        for i in range(0,len(self.Encoder_aux),2):\n",
    "            a = self.Encoder_aux[i](a)\n",
    "            a = self.Encoder_aux[i+1](a)\n",
    "            a = relu(a)\n",
    "            if DROPOUT:\n",
    "                a = dropout(a, p=do_p2)\n",
    "        q_a_mu, q_a_log_var = torch.chunk(a, 2, dim=-1) # divide to mu and sigma\n",
    "        return q_a_mu, q_a_log_var\n",
    "    \n",
    "    def decoder_aux(self,xz,y):\n",
    "        a = torch.cat([xz,y],dim=-1)\n",
    "        for i in range(0,len(self.Decoder_aux),2):\n",
    "            a = self.Decoder_aux[i](a)\n",
    "            a = a.permute(0,2,1)\n",
    "            a = self.Decoder_aux[i+1](a)\n",
    "            a = a.permute(0,2,1)\n",
    "            a = relu(a)\n",
    "            if DROPOUT:\n",
    "                a = dropout(a, p=do_p2)  \n",
    "        return a\n",
    "    \n",
    "    def additional_layer(self,xa,y):\n",
    "        z = torch.cat([xa,y],dim=-1)\n",
    "        z = self.Additional_layer[0](z)\n",
    "        z = z.permute(0,2,1)\n",
    "        z = self.Additional_layer[1](z)\n",
    "        z = z.permute(0,2,1)\n",
    "        z = relu(z)\n",
    "        if DROPOUT:\n",
    "            z = dropout(z, p=do_p2) \n",
    "        return z\n",
    "    \n",
    "    def classifier(self,xa):\n",
    "        for i in range(0,len(self.Classifier),2):\n",
    "            xa = self.Classifier[i](xa)\n",
    "            if i < len(self.Classifier)-1:\n",
    "                if aux_variables > 0:\n",
    "                    xa = xa.permute(0,2,1)\n",
    "                    xa = self.Classifier[i+1](xa)\n",
    "                    xa = xa.permute(0,2,1)\n",
    "                else:\n",
    "                    xa = self.Classifier[i+1](xa)\n",
    "                xa = relu(xa)\n",
    "                if DROPOUT:\n",
    "                    xa = dropout(xa, p=0.3)\n",
    "        return softmax(xa,dim=-1)\n",
    "    \n",
    "    def sample_y(self,batch_size,num_samples,no_classes,i):\n",
    "        tmp = Variable(torch.zeros(no_classes))\n",
    "        tmp[i] = 1\n",
    "        if cuda:\n",
    "            tmp = tmp.cuda()\n",
    "        return tmp.repeat(batch_size,num_samples,1)\n",
    "    \n",
    "    def sample_from_latent(self,x):\n",
    "        x_UL = []\n",
    "        for j in range(No_classes):\n",
    "            tmp = self.decoder(x.unsqueeze(1).repeat(1,num_samples,1), self.sample_y(batch_size,num_samples,No_classes,j))\n",
    "            x_UL.append(tmp)\n",
    "        x_hat, _, _ = output_recon(sum(x_UL))\n",
    "        return x_hat\n",
    "  \n",
    "    \n",
    "    def __init__(self, latent_features, num_samples):\n",
    "        super(CNN_VAE, self).__init__()\n",
    "        \n",
    "        self.latent_features = latent_features\n",
    "        self.num_samples = num_samples\n",
    "        \n",
    "        # Calculate final size of the CNN\n",
    "        self.final_dim = compute_final_dimension(height,width,conv_out_channels[-1],NUM_CONV)\n",
    "        \n",
    "        ## Convolutional layers of the encoder\n",
    "        input_channels = channels\n",
    "        Encoder_conv = nn.ModuleList()\n",
    "        for i in range(NUM_CONV):\n",
    "            Encoder_conv.append(Conv2d( in_channels=input_channels,\n",
    "                                            out_channels=conv_out_channels[i],\n",
    "                                            kernel_size=conv_kernel[i],\n",
    "                                            stride=conv_stride[i],\n",
    "                                            padding=conv_padding[i]))\n",
    "            Encoder_conv.append(BatchNorm2d(conv_out_channels[i], eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "            Encoder_conv.append(MaxPool2d(  kernel_size=pool_kernel, \n",
    "                                        stride=pool_stride,\n",
    "                                        padding=pool_padding,\n",
    "                                        return_indices = False))\n",
    "            input_channels = conv_out_channels[i]\n",
    "        self.add_module(\"Encoder_conv\",Encoder_conv)\n",
    "        \n",
    "        # Fully connected layers of encoder\n",
    "        Encoder_FC = nn.ModuleList()\n",
    "        in_weights = self.final_dim[0]\n",
    "        for i in range(NUM_LIN):\n",
    "            Encoder_FC.append(Linear(in_features=in_weights, out_features=lin_layer[i]))\n",
    "            Encoder_FC.append(BatchNorm1d(lin_layer[i], eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "            in_weights = lin_layer[i]\n",
    "        self.add_module(\"Encoder_FC\",Encoder_FC)\n",
    "        \n",
    "        # map to latent space\n",
    "        Additional_layer = nn.ModuleList()\n",
    "        Additional_layer.append(Linear(in_features=lin_layer[-1]+aux_variables+No_classes, out_features=latent_features*2))\n",
    "        Additional_layer.append(BatchNorm1d(latent_features*2, eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "        self.add_module(\"Additional_layer\",Additional_layer)\n",
    "        \n",
    "        # Auxillary network\n",
    "        if aux_variables > 0:\n",
    "            # Auxillary encoder\n",
    "            Encoder_aux = nn.ModuleList()\n",
    "            in_weights = lin_layer[-1]\n",
    "            for i in range(NUM_AUX):\n",
    "                Encoder_aux.append(Linear(in_features=in_weights, out_features=aux_layer[i]))\n",
    "                Encoder_aux.append(BatchNorm1d(aux_layer[i], eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "                in_weights = aux_layer[i]\n",
    "            Encoder_aux.append(Linear(in_features=aux_layer[-1], out_features=aux_variables*2))\n",
    "            Encoder_aux.append(BatchNorm1d(aux_variables*2, eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "            self.add_module(\"Encoder_aux\", Encoder_aux)\n",
    "            # Auxillary decoder\n",
    "            Decoder_aux = nn.ModuleList()\n",
    "            for i in range(NUM_AUX_DECODER):\n",
    "                if i == 0:\n",
    "                    in_weights = self.latent_features + lin_layer[-1] + No_classes\n",
    "                else:\n",
    "                    in_weights = aux_decoder_layers[i-1]\n",
    "                Decoder_aux.append(Linear(in_features=in_weights, out_features=aux_decoder_layers[i]))\n",
    "                Decoder_aux.append(BatchNorm1d(aux_decoder_layers[i], eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "            Decoder_aux.append(Linear(in_features=aux_decoder_layers[-1], out_features=aux_variables*2))\n",
    "            Decoder_aux.append(BatchNorm1d(aux_variables*2, eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "            self.add_module(\"Decoder_aux\", Decoder_aux)    \n",
    "        \n",
    "        # Initialize fully connected layers from latent space to convolutional layers\n",
    "        Decoder_FC = nn.ModuleList()\n",
    "        Decoder_FC.append(Linear(in_features=latent_features+No_classes, out_features=lin_layer[-1]))\n",
    "        Decoder_FC.append(BatchNorm1d(lin_layer[-1], eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "        for i in reversed(range(NUM_LIN)):\n",
    "            if i == 0:\n",
    "                out_weights = self.final_dim[0]\n",
    "            else:\n",
    "                out_weights = lin_layer[i-1]\n",
    "            Decoder_FC.append(Linear(in_features=lin_layer[i], out_features=out_weights))\n",
    "            Decoder_FC.append(BatchNorm1d(out_weights, eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "        self.add_module(\"Decoder_FC\",Decoder_FC)\n",
    "        \n",
    "        # Convolutional layers of the decoder\n",
    "        Decoder_conv = nn.ModuleList()\n",
    "        for i in reversed(range(NUM_CONV)):\n",
    "            if i == 0:\n",
    "                output_channels = channels*2\n",
    "            else:\n",
    "                output_channels = conv_out_channels[i-1] \n",
    "            Decoder_conv.append(ConvTranspose2d(in_channels=conv_out_channels[i],\n",
    "                                                out_channels=output_channels,\n",
    "                                                kernel_size=conv_kernel[i],\n",
    "                                                stride=conv_stride[i],\n",
    "                                                padding=conv_padding[i]))\n",
    "            Decoder_conv.append(BatchNorm2d(output_channels, eps = batchnorm_eps, momentum = batchnorm_momentum))\n",
    "        self.add_module(\"Decoder_conv\",Decoder_conv)\n",
    "\n",
    "        # Fully connected layers from convolutional layers to classification\n",
    "        Classifier = nn.ModuleList()\n",
    "        if aux_variables > 0:\n",
    "            in_weights = lin_layer[-1]+aux_variables\n",
    "        else:\n",
    "            in_weights = lin_layer[-1]\n",
    "        for i in range(NUM_CLASS):\n",
    "            Classifier.append(Linear(in_features=in_weights, out_features=classifier_layer[i]))\n",
    "            Classifier.append(BatchNorm1d(classifier_layer[i], eps = 1e-4, momentum = batchnorm_momentum))\n",
    "            in_weights = classifier_layer[i]\n",
    "        Classifier.append(Linear(in_features=classifier_layer[-1], out_features = No_classes))\n",
    "        self.add_module(\"Classifier\", Classifier)\n",
    "        \n",
    "        \n",
    "        # Initialize weight of layers\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n",
    "                nn.init.xavier_normal(m.weight.data)\n",
    "                if m.bias is not None:\n",
    "                    m.bias.data.zero_()\n",
    "                \n",
    "### Forward ####\n",
    "    def forward(self, x, y=None):\n",
    "        outputs = {}\n",
    "        self.indices = []\n",
    "        self.layer_size = []\n",
    "        x = self.encoder(x)\n",
    "        if aux_variables > 0:\n",
    "            q_a_mu, q_a_log_var = self.encoder_aux(x)\n",
    "            q_a = gaussian_sample(q_a_mu,q_a_log_var,num_samples,aux_variables) # sample auxillary variables\n",
    "            outputs[\"q_a\"] = q_a # Assign to outputs\n",
    "            xa = torch.cat([x.unsqueeze(1).repeat(1,num_samples,1),q_a],dim=2) # Create combined vector of x and q_a\n",
    "        else:\n",
    "            xa = x\n",
    "        \n",
    "        # Run trough classifier\n",
    "        logits = self.classifier(xa)\n",
    "        \n",
    "        if aux_variables <= 0:\n",
    "            logits = logits.unsqueeze(1)\n",
    "            logits = logits.repeat(1,num_samples,1)\n",
    "            xa = xa.unsqueeze(1).repeat(1,num_samples,1) \n",
    "            \n",
    "        # Map x, y, a to latent space\n",
    "        if y is None:\n",
    "            x_UL = []\n",
    "            for j in range(No_classes):\n",
    "                z = self.additional_layer(xa,self.sample_y(batch_size,num_samples,No_classes,j))\n",
    "                activation = z\n",
    "                x_UL.append(z)\n",
    "            lat_in = sum(x_UL)\n",
    "            del x_UL\n",
    "        else:\n",
    "            lat_in = self.additional_layer(xa,y.unsqueeze(1).repeat(1,num_samples,1))\n",
    "#             activation = lat_in\n",
    "            \n",
    "        # Split into mu and log_var\n",
    "        mu, log_var = torch.chunk(lat_in, 2, dim=-1)\n",
    "        # Make sure that the log variance is positive\n",
    "        log_var = softplus(log_var)\n",
    "        # Sample from latent space\n",
    "        z = gaussian_sample(mu,log_var,num_samples,latent_features)\n",
    "                \n",
    "        # aux. decoder\n",
    "        if aux_variables > 0:\n",
    "            xz = torch.cat([z, x.unsqueeze(1).repeat(1,z.shape[1],1)],dim = -1)\n",
    "            if y is None:\n",
    "                a_UL = []\n",
    "                for j in range(No_classes):\n",
    "                    a_UL.append(self.decoder_aux(xz,self.sample_y(batch_size,num_samples,No_classes,j)))\n",
    "                a_log_var = []\n",
    "                a_mean = []\n",
    "                for j in range(No_classes):\n",
    "                    tmp1, tmp2 = torch.chunk(a_UL[j], 2, dim=-1) # the mean and log_var reconstructions from the decoder\n",
    "                    a_mean.append(tmp1)\n",
    "                    a_log_var.append(softplus(tmp2))\n",
    "                del a_UL, tmp1, tmp2\n",
    "            else:\n",
    "                a = self.decoder_aux(xz,y.unsqueeze(1).repeat(1,num_samples,1))\n",
    "                a_mean, a_log_var = torch.chunk(a, 2, dim=-1) # the mean and log_var reconstructions from the decoder\n",
    "                a_log_var = softplus(a_log_var)    \n",
    "            \n",
    "        # Decoder     \n",
    "        if y is None:\n",
    "            x_UL = []\n",
    "            for j in range(No_classes):\n",
    "                tmp = self.decoder(z, self.sample_y(batch_size,num_samples,No_classes,j))\n",
    "                x_UL.append(tmp)\n",
    "            x_log_var = []\n",
    "            x_mean = []\n",
    "            x_hat = []\n",
    "            for j in range(No_classes):\n",
    "                tmp1, tmp2, tmp3 = output_recon(x_UL[j])\n",
    "                x_hat.append(tmp1)\n",
    "                x_log_var.append(tmp2)\n",
    "                x_mean.append(tmp3)\n",
    "            del x_UL, tmp1, tmp2, tmp3\n",
    "        else:\n",
    "            x = self.decoder(z, y.unsqueeze(1).repeat(1,num_samples,1))\n",
    "            x_hat, x_log_var, x_mean = output_recon(x)\n",
    "        \n",
    "        # Assign variables\n",
    "        outputs[\"x_hat\"] = x_hat # This is used for visulizations only \n",
    "        outputs[\"z\"] = z\n",
    "        outputs[\"mu\"] = mu\n",
    "        outputs[\"log_var\"] = log_var\n",
    "        \n",
    "        # image recontructions (notice they are outputted as matrices)\n",
    "        outputs[\"x_mean\"] =  x_mean #x_hat  # mean reconstructions (for loss!!!)\n",
    "        outputs[\"x_log_var\"] = x_log_var #torch.reshape(x_log_var,(-1,height,width)) # log var reconstructions (for loss!!!)\n",
    "        \n",
    "        # auxillary outputs\n",
    "        if aux_variables > 0:            \n",
    "            outputs[\"q_a_mu\"] = q_a_mu\n",
    "            outputs[\"q_a_log_var\"] = q_a_log_var\n",
    "            outputs[\"p_a_mu\"] = a_mean\n",
    "            outputs[\"p_a_log_var\"] = a_log_var\n",
    "        \n",
    "        # classifier outputs \n",
    "        outputs[\"y_hat\"] = logits\n",
    "        \n",
    "        # Activation of latent features\n",
    "#         outputs[\"activation\"] = activation\n",
    "\n",
    "        return outputs\n",
    "\n",
    "# The number of samples used then initialising the VAE, \n",
    "# is number of samples drawn from the distribution\n",
    "num_samples = 5\n",
    "latent_features = 128\n",
    "\n",
    "net = CNN_VAE(latent_features, num_samples)\n",
    "print(net)\n",
    "\n",
    "# Transfer model to GPU ifavailable\n",
    "if cuda:\n",
    "    net = net.cuda()\n",
    "\n",
    "# Test\n",
    "if 1 == 0:\n",
    "    x = torch.randn(batch_size,1,width, height)\n",
    "    x = Variable(x)\n",
    "    if cuda:\n",
    "        x = x.cuda()\n",
    "        y = None\n",
    "    y = net(x)\n",
    "    print(y['x_hat'][0].shape)\n",
    "\n",
    "if cuda:\n",
    "    print('before: ',torch.cuda.memory_allocated(device=0))\n",
    "    import gc\n",
    "    #del y,x\n",
    "    # gc.collect()\n",
    "    print('after: ',torch.cuda.memory_allocated(device=0))\n",
    "\n",
    "#for parameter in net.parameters():\n",
    "#    print(parameter.shape)\n",
    "#epsilon = torch.randn(batch_size, latent_features).cuda\n",
    "#samples = torch.sigmoid(net.decoder(net.latent_to_CNN(epsilon))).detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "dd92a1ecff915e0aa69a9a2613ef672228917283"
   },
   "outputs": [],
   "source": [
    "from torch.nn.functional import binary_cross_entropy\n",
    "from torch import optim\n",
    "import math\n",
    "\n",
    "def Gaussian_density(sample_img,mu_img,log_var_img):\n",
    "    c = - 0.5 * math.log(2 * math.pi)\n",
    "    density = c - log_var_img/2 - (sample_img - mu_img)**2/(2 * torch.exp(log_var_img))\n",
    "    #density = c -  (sample_img - mu_img)**2/(2 * torch.exp(log_var_img))\n",
    "    #print(\"Density:\",density)\n",
    "    #print(\"Density.shape:\", density.shape)\n",
    "    return torch.sum(density,dim = 1) # Sum over channels\n",
    "\n",
    "def kl_a_calc(q_a,q_mu, q_log_var,p_mu, p_log_var):\n",
    "    # The function assumes: \n",
    "        # q_a has dimension: [batch_size,No_samples,latent_features]\n",
    "        # q_mu/log_var has dimension: [batch_size,latent_features]\n",
    "        # p_mu/log_var has dimension: [batch_size,No_samples,latent_features]\n",
    "        \n",
    "    p_mu      = p_mu.view(batch_size,num_samples,-1)\n",
    "    p_mu      = torch.mean(p_mu, dim = 1)\n",
    "    p_log_var = p_log_var.view(batch_size,num_samples,-1)\n",
    "    p_log_var = torch.mean(p_log_var, dim = 1)\n",
    "    \n",
    "    def log_gaussian(x, mu, log_var):\n",
    "        log_pdf = - 0.5 * math.log(2 * math.pi) - log_var / 2 - (x - mu)**2 / (2 * torch.exp(log_var))\n",
    "        log_pdf = torch.sum(log_pdf, dim=1) # sum over each over the observations (mu + log_var*epsilon)\n",
    "        log_pdf = torch.sum(log_pdf,dim=1) # sum over q_a, i.e. latent features\n",
    "        return log_pdf\n",
    "\n",
    "    # put in middle dimension\n",
    "    q_mu      = q_mu.unsqueeze(1)\n",
    "    q_log_var = q_log_var.unsqueeze(1)\n",
    "    p_mu      = p_mu.unsqueeze_(1)\n",
    "    p_log_var = p_log_var.unsqueeze_(1)\n",
    "    # densities of each disitribution \n",
    "    qz = log_gaussian(q_a,q_mu,q_log_var)\n",
    "    pz = log_gaussian(q_a,p_mu,p_log_var)\n",
    "    # kl divergence\n",
    "    kl = qz - pz\n",
    "    \n",
    "    return kl\n",
    "\n",
    "def ELBO_loss(sample_img, outputs, kl_warmup=None):\n",
    "    # Parameter in deterministic warmup for KL divergence\n",
    "    beta = 1 if kl_warmup is None else kl_warmup\n",
    "    \n",
    "    # Weighting kl's and likelihood\n",
    "    w1 = 0.5\n",
    "    w2 = 0.5\n",
    "    \n",
    "    if type(outputs['x_mean']) == list:\n",
    "        ELBO = []\n",
    "        kl_x = -0.5 * torch.sum(1 + outputs['log_var'] - outputs['mu']**2 - torch.exp(outputs['log_var']), dim=1)\n",
    "        kl_x = torch.sum(kl_x,dim=1) # sum over the features\n",
    "        for j in range(No_classes):\n",
    "            likelihood = Gaussian_density(sample_img, outputs['x_mean'][j], outputs['x_log_var'][j])\n",
    "            if aux_variables > 0:\n",
    "                kl_a = kl_a_calc(outputs[\"q_a\"],outputs[\"q_a_mu\"],outputs[\"q_a_log_var\"],outputs[\"p_a_mu\"][j],outputs[\"p_a_log_var\"][j])\n",
    "                kl = w1 * kl_x + (1 - w1) * kl_a\n",
    "            else:\n",
    "                kl_a = torch.Tensor([0])\n",
    "                kl = kl_x \n",
    "            likelihood = likelihood.view(batch_size, -1)\n",
    "            likelihood = torch.sum(likelihood, dim=1) # Sum over features (224x224 = 50,176)\n",
    "            ELBO.append(w2 * likelihood - (1 - w2) * beta * kl)\n",
    "        \n",
    "        L = torch.cat( (torch.unsqueeze(ELBO[0],1),torch.unsqueeze(ELBO[1],1)),dim =1 )\n",
    "        # Calculate entropy H(q(y|x)) and sum over all labels\n",
    "        logits = torch.mean(outputs['y_hat'],dim = 1)\n",
    "        \n",
    "        H = -torch.sum(torch.mul(logits, torch.log(logits + 1e-8)), dim=-1) \n",
    "        L = torch.sum(torch.mul(logits, L), dim=-1)\n",
    "        \n",
    "        # Equivalent to -U(x)\n",
    "        U = L - H\n",
    "        \n",
    "        #RMS_1 = torch.sqrt(torch.mean((sample_img - outputs['x_mean'][0])**2))\n",
    "        #RMS_2 = torch.sqrt(torch.mean((sample_img - outputs['x_mean'][1])**2))\n",
    "        #U = - torch.sum(RMS_1 + RMS_2)\n",
    "        #U = - ( torch.abs(sample_img - outputs['x_mean'][0])**2 + torch.abs(sample_img - outputs['x_mean'][0])**2)\n",
    "        \n",
    "        return -torch.mean(U), -torch.mean(H), -torch.mean(L),  (1 - w2) * beta * torch.mean(kl), -w2 * torch.mean(likelihood), w1*torch.mean(kl_x), (1-w1)*torch.mean(kl_a)\n",
    "    else:\n",
    "        likelihood = Gaussian_density(sample_img, outputs['x_mean'], outputs['x_log_var'])\n",
    "        kl_x = -0.5 * torch.sum(1 + outputs['log_var'] - outputs['mu']**2 - torch.exp(outputs['log_var']), dim=1)\n",
    "        if aux_variables > 0:\n",
    "            kl_a = kl_a_calc(outputs[\"q_a\"],outputs[\"q_a_mu\"],outputs[\"q_a_log_var\"],outputs[\"p_a_mu\"],outputs[\"p_a_log_var\"])\n",
    "            kl = w1 * torch.mean(kl_x) + (1 - w1) * torch.mean(kl_a)\n",
    "        else:\n",
    "            kl_a = torch.Tensor([0])\n",
    "            kl = torch.mean(kl_x)\n",
    "        likelihood = likelihood.view(batch_size, -1)\n",
    "        likelihood = torch.sum(likelihood, dim=1) # Sum over features (224x224 = 50,176)\n",
    "        ELBO = w2 * torch.mean(likelihood) - (1 - w2) * beta * kl    \n",
    "        # Notice minus sign as we want to maximise ELBO\n",
    "        return -ELBO, (1 - w2) * beta * kl, -w2 * torch.mean(likelihood), w1*torch.mean(kl_x), (1-w1)*torch.mean(kl_a)\n",
    "    \n",
    "    # Regularization error: \n",
    "    # Kulback-Leibler divergence between approximate posterior, q(z|x)\n",
    "    # and prior p(z) = N(z | mu, sigma*I).\n",
    "    \n",
    "    # In the case of the KL-divergence between diagonal covariance Gaussian and \n",
    "    # a standard Gaussian, an analytic solution exists. Using this excerts a lower\n",
    "    # variance estimator of KL(q||p)\n",
    "    # Combining the two terms in the evidence lower bound objective (ELBO) \n",
    "    # mean over batch \n",
    "\n",
    "# Define optimizer: The Adam optimizer works really well with VAEs.\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001)\n",
    "loss_function = ELBO_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "7e691c43f146ec4fb7e8a5157b35d650ecb8fbef",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu:           torch.Size([64, 5, 128]) tensor(0)\n",
      "log_var:      torch.Size([64, 5, 128]) tensor(0)\n",
      "mu_img:       torch.Size([64, 1, 32, 32]) tensor(0)\n",
      "log_var_img:  torch.Size([64, 1, 32, 32]) tensor(0)\n",
      "x:            torch.Size([64, 1, 32, 32]) tensor(0)\n",
      "x_hat:        torch.Size([64, 1, 32, 32]) tensor(0)\n",
      "z:            torch.Size([64, 5, 128]) tensor(0)\n",
      "Total loss:   tensor(699.1784, grad_fn=<NegBackward>)\n",
      "kl:           tensor(0.8562, grad_fn=<MulBackward>)\n",
      "Class. loss:  tensor(337.2842, grad_fn=<SumBackward0>)\n",
      "Likelihood:   tensor(698.3222, grad_fn=<MulBackward>)\n"
     ]
    }
   ],
   "source": [
    "from torch.autograd import Variable\n",
    "import gc\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache\n",
    "\n",
    "x, y = next(iter(train_loader_labelled))\n",
    "u, _ = next(iter(train_loader))\n",
    "\n",
    "y_hot =  torch.zeros([batch_size,2], requires_grad=True)\n",
    "for i in range(len(y)):\n",
    "    y_hot[i] = torch.tensor([0, 1]) if y[i]==1 else torch.tensor([1, 0])\n",
    "\n",
    "x, y, y_hot = Variable(x), Variable(y), Variable(y_hot)\n",
    "if cuda:\n",
    "    # They need to be on the same device and be synchronized.\n",
    "    x, y, y_hot = x.cuda(device=0), y.cuda(device=0), y_hot.cuda(device=0)\n",
    "    u = u.cuda(device=0)\n",
    "\n",
    "outputs = net(u)    \n",
    "#loss, kl, likelihood = loss_function(u,outputs)\n",
    "elbo_u, elbo_H, elbo_L, kl_u, likelihood_u, kl_u_x, kl_u_a= loss_function(u,outputs)\n",
    "outputs = net(x,y_hot)\n",
    "\n",
    "x_hat = outputs[\"x_hat\"]\n",
    "mu, log_var = outputs[\"mu\"], outputs[\"log_var\"]\n",
    "mu_img, log_var_img = outputs[\"x_mean\"], outputs[\"x_log_var\"]\n",
    "z = outputs[\"z\"]\n",
    "logits = outputs[\"y_hat\"]\n",
    "y_hot = y_hot.unsqueeze(dim = 1).repeat(1,logits.shape[1],1)\n",
    "classification_loss = torch.sum(torch.abs(y_hot - logits))\n",
    "\n",
    "#loss, kl = loss_function(x, mu_img, log_var_img, torch.sum(mu,dim = 1), torch.sum(log_var,dim = 1))\n",
    "loss, kl, likelihood, kl_l_x, kl_l_a = loss_function(u,outputs)\n",
    "print('mu:          ',mu.shape,torch.sum(torch.isnan(mu)))\n",
    "print('log_var:     ',log_var.shape,torch.sum(torch.isnan(log_var)))\n",
    "print('mu_img:      ',mu_img.shape,torch.sum(torch.isnan(mu_img)))\n",
    "print('log_var_img: ',log_var_img.shape,torch.sum(torch.isnan(log_var_img)))\n",
    "print('x:           ',x.shape,torch.sum(torch.isnan(x)))\n",
    "print('x_hat:       ',x_hat.shape,torch.sum(torch.isnan(x_hat)))\n",
    "print('z:           ',z.shape,torch.sum(torch.isnan(z)))\n",
    "print('Total loss:  ',loss)\n",
    "print('kl:          ',kl)\n",
    "print('Class. loss: ',classification_loss)\n",
    "print('Likelihood:  ',likelihood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "2b2e7e96bc9272e10b8dacfa083a630c9ae5968c"
   },
   "outputs": [],
   "source": [
    "# Function to normalize a single image\n",
    "def normalize_2(x):\n",
    "    # Input: [1, height, width]\n",
    "    x_shape = x.shape\n",
    "    x = x.view(1,-1)\n",
    "    x = x - torch.min(x)\n",
    "    x = x / (torch.max(x) + 1e-8)\n",
    "    if torch.sum(torch.isnan(x))>0:\n",
    "        print(\"nan of tmp\",torch.sum(torch.isnan(x)))\n",
    "    return x.view(x_shape)\n",
    "\n",
    "# Function to calculate balanced accuracy\n",
    "def balanced_accuracy( logits, y_hot, Do_print=None):\n",
    "    # Input:\n",
    "    # logits: [batch_size, num_samples, no_classes]\n",
    "    # y_hot: [batch_size, num_samples, no_classes]\n",
    "    \n",
    "    logits = logits.cpu().detach()\n",
    "    y_hot = y_hot.cpu().detach()\n",
    "    \n",
    "    logits = logits.view(-1,2)\n",
    "    y_hot = y_hot.view(-1,2)\n",
    "    \n",
    "    TP = torch.sum(y_hot[:,0]*torch.round(logits[:,0]))   # True posistive\n",
    "    FP = torch.sum(y_hot[:,1]*torch.round(logits[:,0]))   # False positive\n",
    "    FN = torch.sum(y_hot[:,0]*torch.round(logits[:,1]))   # False negative\n",
    "    TN = torch.sum(y_hot[:,1]*torch.round(logits[:,1]))   # True negative\n",
    "\n",
    "    P = TP + FN\n",
    "    N = FP + TN\n",
    "    \n",
    "    if Do_print != None:\n",
    "        print(\"TP: \",TP)\n",
    "        print(\"FP: \",FP)\n",
    "        print(\"TN: \",TN)\n",
    "        print(\"FN: \",FN)\n",
    "        print(\"P: \",P)\n",
    "        print(\"N: \",N)\n",
    "\n",
    "    acc = torch.sum(TP/P + TN/N)/2\n",
    "        \n",
    "    return acc\n",
    "\n",
    "# Function to calculate balanced binary crossentropy\n",
    "def balanced_binary_cross_entropy( logits, y_hot):\n",
    "    # Input:\n",
    "    # logits: [batch_size, num_samples, no_classes]\n",
    "    # y_hot: [batch_size, num_samples, no_classes]\n",
    "    \n",
    "    if cuda:\n",
    "        classWeight = torch.FloatTensor([torch.sum(y_hot[:,1,0])/torch.sum(y_hot[:,1,]),\\\n",
    "                                     torch.sum(y_hot[:,1,1])/torch.sum(y_hot[:,1,])]).cuda(device=0)\n",
    "    else:\n",
    "        classWeight = torch.FloatTensor([torch.sum(y_hot[:,1,0])/torch.sum(y_hot[:,1,]),\\\n",
    "                                     torch.sum(y_hot[:,1,1])/torch.sum(y_hot[:,1,])])\n",
    "        \n",
    "    class_loss_0 = 0\n",
    "    class_loss_1 = 0\n",
    "    \n",
    "    for i in range(0, batch_size):\n",
    "        tmp = torch.mean(y_hot, dim = 1)[i][0] \n",
    "        if tmp == 0:\n",
    "            class_loss_0 += torch.nn.functional.binary_cross_entropy(logits[i], y_hot[i])\n",
    "        else:\n",
    "            class_loss_1 += torch.nn.functional.binary_cross_entropy(logits[i], y_hot[i])\n",
    "        \n",
    "    #bal_binary_cross_entropy = classWeight[0]*class_loss_0 + classWeight[1]*class_loss_1\n",
    "    bal_binary_cross_entropy = (0.5/classWeight[0])*class_loss_0 + (0.5/classWeight[1])*class_loss_1\n",
    "        \n",
    "    return bal_binary_cross_entropy/batch_size\n",
    "\n",
    "def balanced_accuracy_test( logits, y_hot, Do_print=None):\n",
    "    # Input:\n",
    "    # logits: [batch_size, num_samples, no_classes]\n",
    "    # y_hot: [batch_size, num_samples, no_classes]\n",
    "    \n",
    "    logits = logits.cpu().detach()\n",
    "    y_hot = y_hot.cpu().detach()\n",
    "    \n",
    "    logits = logits.view(-1,2)\n",
    "    y_hot = y_hot.view(-1,2)\n",
    "    \n",
    "    TP = torch.sum(y_hot[:,0]*torch.round(logits[:,0]))   # True posistive\n",
    "    FP = torch.sum(y_hot[:,1]*torch.round(logits[:,0]))   # False positive\n",
    "    FN = torch.sum(y_hot[:,0]*torch.round(logits[:,1]))   # False negative\n",
    "    TN = torch.sum(y_hot[:,1]*torch.round(logits[:,1]))   # True negative\n",
    "\n",
    "    P = TP + FN\n",
    "    N = FP + TN\n",
    "    \n",
    "    if Do_print != None:\n",
    "        print(\"TP: \",TP)\n",
    "        print(\"FP: \",FP)\n",
    "        print(\"TN: \",TN)\n",
    "        print(\"FN: \",FN)\n",
    "        print(\"P: \",P)\n",
    "        print(\"N: \",N)\n",
    "\n",
    "    acc = torch.sum(TP/P + TN/N)/2\n",
    "        \n",
    "    return acc, TP, FP, FN, TN, P, N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "84f3af7ceb3dc3f3c84e6b8f64bf36920ce6c31b",
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc:  tensor(0.6832)\n",
      "TP:  tensor(150.)\n",
      "FP:  tensor(60.)\n",
      "TN:  tensor(75.)\n",
      "FN:  tensor(35.)\n",
      "P:  tensor(185.)\n",
      "N:  tensor(135.)\n"
     ]
    }
   ],
   "source": [
    "## import warnings\n",
    "#warnings.filterwarnings(\"ignore\")\n",
    "torch.cuda.empty_cache\n",
    "# Deleting variable Image and reload package Image\n",
    "%reset_selective -f \"^Image$\"\n",
    "from IPython.display import Image\n",
    "from sklearn.manifold import TSNE\n",
    "import sys\n",
    "\n",
    "\n",
    "import os\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "num_epochs = 21 # No_samples_train // batch_size\n",
    "batch_epo_u = No_samples_train // batch_size\n",
    "batch_epo_l = No_samples_train_labelled // batch_size\n",
    "batch_per_epoch = batch_epo_u if batch_epo_u>batch_epo_l else batch_epo_l\n",
    "\n",
    "\n",
    "tmp_img = \"tmp_vae_out.png\"\n",
    "show_sampling_points = False\n",
    "classes = [0,1]\n",
    "\n",
    "train_loss, valid_loss = [], []\n",
    "train_kl, valid_kl = [], []\n",
    "train_likelihood, valid_likelihood = [], []\n",
    "train_classification, valid_classification = [], []\n",
    "train_acc, valid_acc = [], []\n",
    "train_L, train_H, train_kl_u_x, train_kl_u_a, train_elbo_l, train_kl_l_x, train_kl_l_a = [], [], [], [], [], [], []\n",
    "valid_L, valid_H, valid_kl_u_x, valid_kl_u_a, valid_elbo_l, valid_kl_l_x, valid_kl_l_a = [], [], [], [], [], [], []\n",
    "# train_latent_loss, valid_latent_loss = [], []\n",
    "total_loss = []\n",
    "\n",
    "# Classification Loss\n",
    "alpha = 10 * (No_samples_train + No_samples_train_labelled) / No_samples_train_labelled   \n",
    "print(\"Weight of classification loss (alpha): \", alpha)\n",
    "\n",
    "# Latent Loss\n",
    "beta = 0   \n",
    "\n",
    "# Deterministic Warm-Up\n",
    "deterministic_increment = 75  # The increment in the deterministic warmup. n = 10 adds 1/10 to kl_warmup for every epoch.\n",
    "t_max = 1 # Maximal weight of KL divergence \n",
    "\n",
    "device = torch.device(\"cuda:0\" if cuda else \"cpu\")\n",
    "print(\"Using device:\", device)\n",
    "\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    batch_loss, batch_kl, batch_likelihood, batch_acc, batch_classification = [], [], [], [], []\n",
    "    batch_L, batch_H, batch_kl_u_x, batch_kl_u_a, batch_elbo_l, batch_kl_l_x, batch_kl_l_a = [], [], [], [], [], [], []\n",
    "#     batch_latent_loss = []\n",
    "    net.train()\n",
    "    \n",
    "    # Deterministic Warmup for KL divergence\n",
    "    #kl_warmup = 1\n",
    "    if epoch >= deterministic_increment:\n",
    "        kl_warmup = 1\n",
    "    else:\n",
    "        kl_warmup = epoch * 1/deterministic_increment            \n",
    "    print(\"KL Warm-Up: \", kl_warmup)\n",
    "    ###############################\n",
    "    \n",
    "    # Go through each batch in the training dataset using the loader\n",
    "    # Note that y is not necessarily known as it is here\n",
    "    count = 0\n",
    "    total_loss_batch = 0\n",
    "    for (x, y), (u, _) in zip(cycle(train_loader_labelled), train_loader):\n",
    "        y_hot =  torch.zeros([batch_size,2])\n",
    "        for i in range(len(y)):\n",
    "            y_hot[i] = torch.tensor([0, 1]) if y[i]==1 else torch.tensor([1, 0])\n",
    "            \n",
    "        x, y, u, y_hot = Variable(x), Variable(y), Variable(u), Variable(y_hot)\n",
    "        if cuda:\n",
    "            # They need to be on the same device and be synchronized.\n",
    "            x, y, y_hot = x.cuda(device=0), y.cuda(device=0), y_hot.cuda(device=0)\n",
    "            u = u.cuda(device=0)\n",
    "\n",
    "        count = count + 1\n",
    "        if not count % (batch_per_epoch/4):\n",
    "            print(\"Epoch:\", epoch, \"Batch:\", count,\"/\",batch_per_epoch)\n",
    "\n",
    "        #### Unlabelled\n",
    "        outputs = net(u)\n",
    "        elbo_u, elbo_H, elbo_L, kl_u, likelihood_u, kl_u_x, kl_u_a = loss_function(u, outputs, kl_warmup)\n",
    "        elbo_H = elbo_H.cpu().detach().numpy()\n",
    "        elbo_L = elbo_L.cpu().detach().numpy()\n",
    "        kl_u_x = kl_u_x.cpu().detach().numpy()\n",
    "        kl_u_a = kl_u_a.cpu().detach().numpy()\n",
    "        \n",
    "        #### Labelled\n",
    "        outputs = net(x,y_hot)\n",
    "        logits = outputs[\"y_hat\"]\n",
    "        y_hot = y_hot.unsqueeze(dim = 1).repeat(1,logits.shape[1],1)\n",
    "        \n",
    "        # Weight the classification loss for the two classes\n",
    "        classification_loss =  alpha * balanced_binary_cross_entropy( logits, y_hot)\n",
    "        acc = balanced_accuracy( logits, y_hot)\n",
    "        batch_acc.append(100*acc)\n",
    "        elbo_l, kl_l, likelihood_l, kl_l_x, kl_l_a = loss_function(x, outputs, kl_warmup)\n",
    "        kl_l_x = kl_l_x.cpu().detach().numpy()\n",
    "        kl_l_a = kl_l_a.cpu().detach().numpy()\n",
    "        \n",
    "        #### Latent lose\n",
    "#         rho = torch.tensor(0.05)\n",
    "#         act = outputs[\"activation\"]\n",
    "#         act = (act-torch.min(act)) / torch.max(act-torch.min(act))\n",
    "#         # Mean over batches (training set): from [batch_size, num_samples, num_hidden_units] -> [num_samples, num_hidden_units]\n",
    "#         rho_hat = torch.mean(torch.abs(act), dim = 0)\n",
    "#         # Sum over num_samples and num_hidden_units\n",
    "#         latent_loss = beta * torch.torch.sum(torch.abs(rho * torch.log( rho / rho_hat ) \n",
    "#                                           + (1 - rho) * torch.log( (1-rho) / (1-rho_hat)))).cuda(device=0)\n",
    "\n",
    "        \n",
    "        ### Combine losses \n",
    "        loss =  elbo_l + elbo_u + classification_loss #+ latent_loss # notice alpha has been moved to where classification loss is calculated\n",
    "        total_loss_batch += loss.item()\n",
    "        \n",
    "        ### Optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        elbo_l = elbo_l.cpu().detach().numpy()\n",
    "        batch_classification.append(classification_loss.item())\n",
    "        batch_loss.append(loss.item())\n",
    "        batch_kl.append(kl_u.item()+kl_l.item())\n",
    "        batch_likelihood.append(likelihood_u.item() + likelihood_l.item())\n",
    "        batch_L.append(elbo_L) # NOT elbo_l, but L from elbo_u\n",
    "        batch_H.append(elbo_H)\n",
    "        batch_kl_u_x.append(kl_u_x)\n",
    "        batch_kl_u_a.append(kl_u_a)\n",
    "        batch_elbo_l.append(elbo_l)\n",
    "        batch_kl_l_x.append(kl_l_x)\n",
    "        batch_kl_l_a.append(kl_l_a)\n",
    "#         latent_loss = latent_loss.cpu().detach().numpy()\n",
    "#         batch_latent_loss.append(latent_loss)\n",
    "    \n",
    "    train_classification.append(np.mean(batch_classification))\n",
    "    train_acc.append(np.mean(batch_acc))    \n",
    "    train_loss.append(np.mean(batch_loss))\n",
    "    train_kl.append(np.mean(batch_kl))\n",
    "    train_likelihood.append(np.mean(batch_likelihood))\n",
    "    total_loss.append(total_loss_batch / (batch_size*batch_per_epoch))\n",
    "    train_L.append(np.mean(batch_L))\n",
    "    train_H.append(np.mean(batch_H))\n",
    "    train_kl_u_x.append(np.mean(batch_kl_u_x))\n",
    "    train_kl_u_a.append(np.mean(batch_kl_u_a))\n",
    "    train_elbo_l.append(np.mean(batch_elbo_l))\n",
    "    train_kl_l_x.append(np.mean(batch_kl_l_x))\n",
    "    train_kl_l_a.append(np.mean(batch_kl_l_a))\n",
    "#     train_latent_loss.append(np.mean(batch_latent_loss))\n",
    "    \n",
    "    \n",
    "    # Evaluate, do not propagate gradients\n",
    "    with torch.no_grad():\n",
    "        net.eval()\n",
    "        \n",
    "        # Just load a single batch from the test loader\n",
    "        x, y = next(iter(test_loader))\n",
    "        \n",
    "        y_hot =  torch.zeros([batch_size,2])\n",
    "        for i in range(len(y)):\n",
    "            y_hot[i] = torch.tensor([0, 1]) if y[i]==1 else torch.tensor([1, 0])\n",
    "        x, y, y_hot = Variable(x), Variable(y), Variable(y_hot)\n",
    "        if cuda:\n",
    "            # They need to be on the same device and be synchronized.\n",
    "            x, y_hot = x.cuda(device=0), y_hot.cuda(device=0)\n",
    "        \n",
    "        #### Unlabelled\n",
    "        outputs = net(u)\n",
    "        elbo_u, elbo_H, elbo_L, kl_u, likelihood_u, kl_u_x, kl_u_a = loss_function(u, outputs, kl_warmup)\n",
    "        elbo_H = elbo_H.cpu().detach().numpy()\n",
    "        elbo_L = elbo_L.cpu().detach().numpy()\n",
    "        kl_u_x = kl_u_x.cpu().detach().numpy()\n",
    "        kl_u_a = kl_u_a.cpu().detach().numpy()\n",
    "        \n",
    "        #### Labelled\n",
    "        outputs = net(x,y_hot)\n",
    "        x_hat = outputs['x_hat']\n",
    "        logits = outputs[\"y_hat\"]\n",
    "        z = outputs[\"z\"]\n",
    "        \n",
    "        y_hot = y_hot.unsqueeze(dim = 1).repeat(1,logits.shape[1],1)\n",
    "        #acc = torch.sum(y_hot.view(-1,2) * logits.view(-1,2), dim = 1).cpu().detach().numpy()\n",
    "        #valid_acc.append(sum(acc > 0.5) / batch_size*num_samples)\n",
    "        acc = balanced_accuracy( logits, y_hot)\n",
    "        valid_acc.append(100*acc)\n",
    "        \n",
    "        # Weight the classification loss for the two classes\n",
    "        #classWeight = torch.FloatTensor([torch.sum(y_hot[:,1,0])/torch.sum(y_hot[:,1,]), torch.sum(y_hot[:,1,1])/torch.sum(y_hot[:,1,])]).cuda(device=0)\n",
    "        #classification_loss = alpha*torch.nn.functional.binary_cross_entropy(logits,y_hot, weight=classWeight)\n",
    "        #classification_loss = torch.sum(torch.abs(y_hot - logits))\n",
    "        #classification_loss = alpha*torch.nn.functional.binary_cross_entropy(logits,y_hot)\n",
    "        classification_loss = alpha * balanced_binary_cross_entropy( logits, y_hot)\n",
    "        # elbo, kl = loss_function(x_hat, x, mu, log_var)\n",
    "        elbo_l, kl_l, likelihood_l, kl_l_x, kl_l_a = loss_function(x, outputs, kl_warmup)\n",
    "        kl_l_x = kl_l_x.cpu().detach().numpy()\n",
    "        kl_l_a = kl_l_a.cpu().detach().numpy()\n",
    "        \n",
    "        # We save the latent variable and reconstruction for later use\n",
    "        # we will need them on the CPU to plot\n",
    "        x = x.to(\"cpu\")\n",
    "        x_hat = x_hat.to(\"cpu\")\n",
    "        z = z.detach().to(\"cpu\").numpy()\n",
    "        \n",
    "    \n",
    "        valid_classification.append(classification_loss.item())\n",
    "        valid_loss.append(elbo_l.item()+classification_loss.item()+elbo_u.item())\n",
    "        valid_kl.append(kl_l.item())\n",
    "        valid_likelihood.append(likelihood_l.item())\n",
    "        valid_L.append(elbo_L)\n",
    "        valid_H.append(elbo_H)\n",
    "        valid_kl_u_x.append(kl_u_x)\n",
    "        valid_kl_u_a.append(kl_u_a)\n",
    "        valid_elbo_l.append(elbo_l.item())\n",
    "        valid_kl_l_x.append(kl_l_x)\n",
    "        valid_kl_l_a.append(kl_l_a)\n",
    "        \n",
    "    \n",
    "    if epoch == 0:\n",
    "        continue\n",
    "    \n",
    "    if epoch % 10:\n",
    "        continue\n",
    "    \n",
    "    validation_from = min(20, num_epochs)\n",
    "    if epoch < validation_from:\n",
    "        VALIDATION = False\n",
    "    else:\n",
    "        VALIDATION = True\n",
    "    \n",
    "    # -- Plotting --\n",
    "    f, axarr = plt.subplots(5, 2, figsize=(20, 30))\n",
    "\n",
    "    # Loss\n",
    "    ax = axarr[0, 0]\n",
    "    ax.set_title(\"ELBO\")\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Error')\n",
    "\n",
    "    ax.plot(np.arange(epoch+1), train_loss, color=\"black\")\n",
    "    if VALIDATION:\n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_loss[validation_from:len(valid_loss)], color=\"gray\", linestyle=\"--\")\n",
    "        ax.legend(['Training', 'Validation'])\n",
    "    else:\n",
    "        ax.legend(['Training'])\n",
    "        \n",
    "        \n",
    "    # Latent space\n",
    "    ax = axarr[0, 1]\n",
    "    ax.set_title('Latent space')\n",
    "    ax.set_xlabel('Dimension 1')\n",
    "    ax.set_ylabel('Dimension 2')\n",
    "    \n",
    "    if batch_size > 4:\n",
    "        rows = 4\n",
    "        columns = batch_size // rows if batch_size // rows < 8 else 8            \n",
    "    else:\n",
    "        rows = 2\n",
    "        columns = 2\n",
    "    \n",
    "    span = np.linspace(-4, 4, rows)\n",
    "    grid = np.dstack(np.meshgrid(span, span)).reshape(-1, 2)\n",
    "    \n",
    "    # If you want to use a dimensionality reduction method you can use\n",
    "    # for example PCA by projecting on two principal dimensions\n",
    "\n",
    "    \"\"\" PCA \"\"\"\n",
    "    #z = PCA(n_components=2).fit_transform(z.reshape(-1,latent_features))\n",
    "    #z = z.reshape(batch_size,num_samples,2)\n",
    "    \n",
    "    \"\"\" TSNE \"\"\"\n",
    "    z = TSNE(n_components=2).fit_transform(z.reshape(-1,latent_features))\n",
    "    z = z.reshape(batch_size,num_samples,2)\n",
    "    #z = z[:,1,] # We do only want to plot one z instead of |num_samples|\n",
    "    \n",
    "    colors = iter(plt.get_cmap('Set1')(np.linspace(0, 1.0, len(classes))))\n",
    "    for c in classes:\n",
    "        ax.scatter(*z[c == y.numpy()].reshape(-1, 2).T, c=next(colors), marker='o', label=c)\n",
    "        \n",
    "    if show_sampling_points:\n",
    "        ax.scatter(*grid.T, color=\"k\", marker=\"x\", alpha=0.5, label=\"Sampling points\")\n",
    "\n",
    "    ax.legend()\n",
    "    \n",
    "    # KL / reconstruction\n",
    "    ax = axarr[1, 0]\n",
    "    \n",
    "    ax.set_title(\"Losses\")\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Loss')\n",
    "\n",
    "    ax.set_title(\"ELBO: Labelled and unlabelled\")\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Loss')        \n",
    "    if VALIDATION:\n",
    "        ax.plot(np.arange(epoch+1), train_elbo_l, color=\"red\")\n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_elbo_l[validation_from:len(valid_loss)], color=\"red\", linestyle=\"--\")\n",
    "        ax.plot(np.arange(epoch+1), train_L, color=\"green\")  \n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_L[validation_from:len(valid_loss)], color=\"green\", linestyle=\"--\") \n",
    "        ax.legend(['Train. ELBO Labelled',  'Valid. ELBO Labelled', 'Train. ELBO Unlabelled', 'Valid. ELBO Unlabelled'])\n",
    "    else:\n",
    "        ax.plot(np.arange(epoch+1), train_elbo_l, color=\"red\")\n",
    "        ax.plot(np.arange(epoch+1), train_L, color=\"green\")    # Typically L >>> H, therefore not L+H   \n",
    "        ax.legend(['Train. ELBO Labelled', 'Train. ELBO Unlabelled'])\n",
    "    \n",
    "    # Latent space samples\n",
    "    ax = axarr[1, 1]\n",
    "    ax.set_title('Samples from latent space')\n",
    "    ax.axis('off')\n",
    "\n",
    "    with torch.no_grad():\n",
    "        epsilon = torch.randn(batch_size, latent_features).to(device)\n",
    "        samples = torch.sigmoid(net.sample_from_latent(epsilon)).cpu().detach().numpy()\n",
    "\n",
    "    canvas = np.zeros((IMG_SIZE*rows, columns*IMG_SIZE))\n",
    "    for i in range(rows):\n",
    "        for j in range(columns):\n",
    "            idx = i % columns + rows * j\n",
    "            #temp_img = samples[idx].reshape((224, 224))\n",
    "            #canvas[i*28:(i+1)*28, j*28:(j+1)*28] = resize(temp_img, output_shape=[28,28], mode='reflect', anti_aliasing=True)\n",
    "            canvas[i*IMG_SIZE:(i+1)*IMG_SIZE, j*IMG_SIZE:(j+1)*IMG_SIZE] = samples[idx,0:IMG_SIZE**2].reshape((IMG_SIZE, IMG_SIZE))\n",
    "    ax.imshow(canvas, cmap='gray')\n",
    "\n",
    "    # Inputs\n",
    "    ax = axarr[2, 0]\n",
    "    ax.set_title('Inputs')\n",
    "    ax.axis('off')\n",
    "\n",
    "    canvas = np.zeros((IMG_SIZE*rows, columns*IMG_SIZE))\n",
    "    for i in range(rows):\n",
    "        for j in range(columns):\n",
    "            idx = i % columns + rows * j\n",
    "            #temp_img = x[idx].reshape((224, 224))\n",
    "            #canvas[i*28:(i+1)*28, j*28:(j+1)*28] = resize(temp_img, output_shape=[28,28], mode='reflect', anti_aliasing=True)\n",
    "            canvas[i*IMG_SIZE:(i+1)*IMG_SIZE, j*IMG_SIZE:(j+1)*IMG_SIZE] = x[idx,0:IMG_SIZE**2].reshape((IMG_SIZE, IMG_SIZE))\n",
    "    ax.imshow(canvas, cmap='gray')\n",
    "\n",
    "    # Reconstructions\n",
    "    ax = axarr[2, 1]\n",
    "    ax.set_title('Reconstructions: mean')\n",
    "    ax.axis('off')\n",
    "\n",
    "    canvas = np.zeros((IMG_SIZE*rows, columns*IMG_SIZE))\n",
    "    for i in range(rows):\n",
    "        for j in range(columns):\n",
    "            idx = i % columns + rows * j\n",
    "            #temp_img = x_hat[idx].reshape((224, 224))\n",
    "            #canvas[i*28:(i+1)*28, j*28:(j+1)*28] = resize(temp_img, output_shape=[28,28], mode='reflect', anti_aliasing=True)\n",
    "            canvas[i*IMG_SIZE:(i+1)*IMG_SIZE, j*IMG_SIZE:(j+1)*IMG_SIZE] = normalize_2(x_hat[idx,0:IMG_SIZE**2].reshape((IMG_SIZE, IMG_SIZE)))\n",
    "    ax.imshow(canvas, cmap='gray')\n",
    "\n",
    "    # Reconstructions\n",
    "    ax = axarr[3, 0]\n",
    "    ax.axis('off')\n",
    "    #ax.set_title('Reconstructions: sigma')\n",
    "    #sigma = (torch.exp(outputs[\"x_log_var\"]/2)).detach().to(\"cpu\").numpy()\n",
    "    #canvas = np.zeros((IMG_SIZE*rows, columns*IMG_SIZE))\n",
    "    #for i in range(rows):\n",
    "    #    for j in range(columns):\n",
    "    #        idx = i % columns + rows * j\n",
    "    #        #temp_img = x_hat[idx].reshape((224, 224))\n",
    "    #        #canvas[i*28:(i+1)*28, j*28:(j+1)*28] = resize(temp_img, output_shape=[28,28], mode='reflect', anti_aliasing=True)\n",
    "    #        canvas[i*IMG_SIZE:(i+1)*IMG_SIZE, j*IMG_SIZE:(j+1)*IMG_SIZE] = sigma[idx,0:IMG_SIZE**2].reshape((IMG_SIZE, IMG_SIZE))\n",
    "    #ax.imshow(canvas, cmap='gray')\n",
    "    ax.set_title('Displaying latent features')\n",
    "    with torch.no_grad():\n",
    "        latent_feature = torch.zeros(batch_size,latent_features).to(device)\n",
    "        for i in range(0,latent_features if latent_features<= batch_size else batch_size):\n",
    "            latent_feature[i,i] = 100\n",
    "        displaying_latent_features = (net.sample_from_latent(latent_feature)).cpu().detach() #no sigmoid, instead normalize later\n",
    "        \n",
    "    if displaying_latent_features.shape[0] == 64: \n",
    "        rows_2 = 8\n",
    "        columns_2 = 8\n",
    "    elif displaying_latent_features.shape[0] == 128: \n",
    "        rows_2 = 8\n",
    "        columns_2 = 16\n",
    "    else:\n",
    "        rows_2 = rows\n",
    "        columns_2 = columns   \n",
    "    canvas = np.zeros((IMG_SIZE*rows_2, columns_2*IMG_SIZE))  \n",
    "    for i in range(rows_2):\n",
    "        for j in range(columns_2):\n",
    "            idx = i % columns_2 + rows_2 * j\n",
    "            canvas[i*IMG_SIZE:(i+1)*IMG_SIZE, j*IMG_SIZE:(j+1)*IMG_SIZE] = normalize_2(displaying_latent_features[idx,0:IMG_SIZE**2].reshape((IMG_SIZE, IMG_SIZE)))\n",
    "    ax.imshow(canvas, cmap='gray')\n",
    "    \n",
    "                \n",
    "    # Classification Loss\n",
    "    ax = axarr[3, 1]\n",
    "    ax.set_title(\"Classification Accuracy\")\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Accuracy (%)')\n",
    "    \n",
    "    ax.plot(np.arange(epoch+1), train_acc, color=\"black\")\n",
    "    ax.plot(np.arange(epoch+1), valid_acc, color=\"gray\", linestyle=\"--\")\n",
    "    ax.legend(['Training', 'Validation'])\n",
    "    \n",
    "    # KL Divergence - Unlabelled\n",
    "    ax = axarr[4,0]\n",
    "    ax.set_title(\"KL Divergence - Unlabelled\")\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Loss')\n",
    "    if VALIDATION:\n",
    "        ax.plot(np.arange(epoch+1), train_kl_u_x, color=\"blue\")\n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_kl_u_x[validation_from:len(valid_loss)], color=\"blue\", linestyle=\"--\")\n",
    "        ax.plot(np.arange(epoch+1), train_kl_u_a, color=\"orange\")\n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_kl_u_a[validation_from:len(valid_loss)], color=\"orange\", linestyle=\"--\")\n",
    "        ax.legend(['Train. kl_u_x',  'Valid. kl_u_x', 'Train. kl_u_a', 'Valid. kl_u_a'])\n",
    "    else:\n",
    "        ax.plot(np.arange(epoch+1), train_kl_u_x, color=\"blue\")\n",
    "        ax.plot(np.arange(epoch+1), train_kl_u_a, color=\"orange\")\n",
    "        ax.legend(['Train. kl_u_x',  'Train. kl_u_a'])\n",
    "\n",
    "    # KL Divergence - Labelled\n",
    "    ax = axarr[4,1]\n",
    "    ax.set_title(\"KL Divergence - Labelled\")\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Loss')\n",
    "    if VALIDATION:\n",
    "        ax.plot(np.arange(epoch+1), train_kl_l_x, color=\"blue\")\n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_kl_l_x[validation_from:len(valid_loss)], color=\"blue\", linestyle=\"--\")\n",
    "        ax.plot(np.arange(epoch+1), train_kl_l_a, color=\"orange\")\n",
    "        ax.plot(np.arange(epoch+1)[validation_from:len(valid_loss)], valid_kl_l_a[validation_from:len(valid_loss)], color=\"orange\", linestyle=\"--\")\n",
    "        #ax.plot(np.arange(epoch+1), train_latent_loss, color=\"green\")\n",
    "        ax.legend(['Train. kl_l_x',   'Valid. kl_l_x', 'Train. kl_l_a', 'Valid. kl_l_a'])\n",
    "        #ax.legend(['Train. kl_l_x',   'Valid. kl_l_x', 'Train. kl_l_a', 'Valid. kl_l_a', 'Latent_loss'])\n",
    "    else:\n",
    "        ax.plot(np.arange(epoch+1), train_kl_l_x, color=\"blue\")\n",
    "        ax.plot(np.arange(epoch+1), train_kl_l_a, color=\"orange\")\n",
    "        #ax.plot(np.arange(epoch+1), train_latent_loss, color=\"green\")\n",
    "        ax.legend(['Train. kl_l_x',  'Train. kl_l_a'])\n",
    "        #ax.legend(['Train. kl_l_x',  'Train. kl_l_a',  'Latent_loss'])\n",
    "    \n",
    "    clear_output(wait=True)\n",
    "    plt.savefig(tmp_img)\n",
    "    plt.close(f)\n",
    "    display(Image(filename=tmp_img))\n",
    "\n",
    "    os.remove(tmp_img)\n",
    "    \n",
    "    if epoch % 20:\n",
    "        continue\n",
    "    \n",
    "    # Run trough full test data set\n",
    "    TP, FP, FN, TN, P, N = 0, 0, 0, 0, 0, 0\n",
    "    for (x, y) in test_loader:\n",
    "        y_hot =  torch.zeros([batch_size,2])\n",
    "\n",
    "        for i in range(len(y)):\n",
    "            y_hot[i] = torch.tensor([0, 1]) if y[i]==1 else torch.tensor([1, 0])\n",
    "        x, y, y_hot = Variable(x), Variable(y), Variable(y_hot)\n",
    "        if cuda:\n",
    "            # Must be synchronized on the same device\n",
    "            x, y_hot = x.cuda(device=0), y_hot.cuda(device=0)\n",
    "\n",
    "        #### Labelled\n",
    "        outputs = net(x,y_hot)\n",
    "        logits = outputs[\"y_hat\"]\n",
    "        y_hot = y_hot.unsqueeze(dim = 1).repeat(1,logits.shape[1],1)\n",
    "        acc_1, TP_1, FP_1, FN_1, TN_1, P_1, N_1 = balanced_accuracy_test(logits, y_hot)\n",
    "        TP += TP_1\n",
    "        FP += FP_1\n",
    "        FN += FN_1\n",
    "        TN += TN_1\n",
    "        P  += P_1\n",
    "        N  += N_1\n",
    "\n",
    "    acc =  torch.sum(TP/P + TN/N)/2  \n",
    "    print(\"Acc: \",acc)    \n",
    "    print(\"TP: \",TP)\n",
    "    print(\"FP: \",FP)\n",
    "    print(\"TN: \",TN)\n",
    "    print(\"FN: \",FN)\n",
    "    print(\"P: \",P)\n",
    "    print(\"N: \",N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "6fcf669e3e61e9338cd6df370bdac88f2a0d4688",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc:  tensor(0.6832)\n",
      "TP:  tensor(150.)\n",
      "FP:  tensor(60.)\n",
      "TN:  tensor(75.)\n",
      "FN:  tensor(35.)\n",
      "P:  tensor(185.)\n",
      "N:  tensor(135.)\n"
     ]
    }
   ],
   "source": [
    "# Run trough full test data set\n",
    "TP, FP, FN, TN, P, N = 0, 0, 0, 0, 0, 0\n",
    "for (x, y) in test_loader:\n",
    "    y_hot =  torch.zeros([batch_size,2])\n",
    "    \n",
    "    for i in range(len(y)):\n",
    "        y_hot[i] = torch.tensor([0, 1]) if y[i]==1 else torch.tensor([1, 0])\n",
    "    x, y, y_hot = Variable(x), Variable(y), Variable(y_hot)\n",
    "    if cuda:\n",
    "        # Must be synchronized on the same device\n",
    "        x, y_hot = x.cuda(device=0), y_hot.cuda(device=0)\n",
    "\n",
    "    #### Labelled\n",
    "    outputs = net(x,y_hot)\n",
    "    logits = outputs[\"y_hat\"]\n",
    "    y_hot = y_hot.unsqueeze(dim = 1).repeat(1,logits.shape[1],1)\n",
    "    acc_1, TP_1, FP_1, FN_1, TN_1, P_1, N_1 = balanced_accuracy_test(logits, y_hot)\n",
    "    TP += TP_1\n",
    "    FP += FP_1\n",
    "    FN += FN_1\n",
    "    TN += TN_1\n",
    "    P  += P_1\n",
    "    N  += N_1\n",
    "\n",
    "acc =  torch.sum(TP/P + TN/N)/2  \n",
    "print(\"Acc: \",acc)    \n",
    "print(\"TP: \",TP)\n",
    "print(\"FP: \",FP)\n",
    "print(\"TN: \",TN)\n",
    "print(\"FN: \",FN)\n",
    "print(\"P: \",P)\n",
    "print(\"N: \",N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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