constructs/UE_Introduction/AI_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AI segmentation construct\n",
    "\n",
    "## aims\n",
    "\n",
    "In this tutorial you will \n",
    "1. Watch tutorials on segmentation of fluorescence images using AI\n",
    "2. Answer questionnaire\n",
    "3. Organization and vizualization of the data\n",
    "4. Reconstruction of nuclei shape with star-convex polygons\n",
    "5. Training of a StarDist model for nuclei segmentation\n",
    "6. Evaluation and testing of the network\n",
    "7. More resources\n",
    "\n",
    "## warnings \n",
    "\n",
    "Make sure MPLBACKEND is not defined as `agg` otherwise you will see no graphical output!\n",
    "To make sure of this, run the following command before invoking `jupyter lab`:\n",
    "\n",
    "```sh\n",
    "export MPLBACKEND=\n",
    "```\n",
    "or use the following parameters when importing matplotlib : \n",
    "\n",
    "import matplotlib <br>\n",
    "import matplotlib.pyplot as plt <br>\n",
    "%matplotlib inline <br>\n",
    "%config InlineBackend.figure_format = 'retina' <br>\n",
    "\n",
    "\n",
    "## credits\n",
    "\n",
    "Marcelo Nollmann & JB Fiche, August 2021 <br>\n",
    "Uwe Schmidt https://github.com/stardist/stardist/ <br>\n",
    "Anna Kreshuk https://github.com/kreshuklab/teaching-dl-course-2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 - Preliminary requirement\n",
    "\n",
    "Make sure you are confortable with image manipulation with python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Resources on StarDist and Deep Learning\n",
    "\n",
    "- A quick introduction to Deep Learning : https://www.youtube.com/watch?v=5tvmMX8r_OM \n",
    "- A detailed presentation of StarDist network by Martin Weigert (first 30 minutes of the viedo): https://www.youtube.com/watch?v=Amn_eHRGX5M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Questionnaire\n",
    "\n",
    "Plase answer the following questions\n",
    "- What is the difference between Training and Testing for Deep Learning?\n",
    "- What does epoch stands for?\n",
    "- What is the loss function?\n",
    "- What is a convolution network and why it is interesting for image analysis?\n",
    "- StarDist is performing instance image segmentation. What does it mean?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Organization and vizualization of the data\n",
    "\n",
    "A selection of fluorescence 2D images of nuclei stained with DAPI was prepared. Each original fluorescence image is paired with a mask, an image where all the nuclei have been manually segmented. The masks define the ground-truth, the ideal representation from which the network will learn how to recognize nuclei. \n",
    "Below, we are downloading a training set containing 50 pairs of images and masks for the training of StarDist. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files found, nothing to download.\n",
      "There is a total of 447 images for the training set and 50 images for the testing.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, unicode_literals, absolute_import, division\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams[\"image.interpolation\"] = None\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from glob import glob\n",
    "from tifffile import imread\n",
    "from csbdeep.utils import Path, download_and_extract_zip_file\n",
    "\n",
    "from stardist import fill_label_holes, random_label_cmap\n",
    "lbl_cmap = random_label_cmap()\n",
    "\n",
    "# Download the data originally used for the StarDist paper. The data are organized in two subsets :\n",
    "# - the training data, used to train the network\n",
    "# - the testing data, used to test whether the network was properly trained and is able to segment new images\n",
    "# For each set, two types of images are available :\n",
    "# - the raw data (X, X_test), i.e. original fluorescence images of nuclei in our case\n",
    "# - the ground-truth or label or mask (Y, Y_test), i.e. the annotated images where the nuclei have been manually segmented.\n",
    "\n",
    "download_and_extract_zip_file(\n",
    "    url       = 'https://github.com/stardist/stardist/releases/download/0.1.0/dsb2018.zip',\n",
    "    targetdir = 'data',\n",
    "    verbose   = 1,\n",
    ")\n",
    "\n",
    "X = sorted(glob('data/dsb2018/train/images/*.tif'))\n",
    "Y = sorted(glob('data/dsb2018/train/masks/*.tif'))\n",
    "X_test = sorted(glob('data/dsb2018/test/images/*.tif'))\n",
    "Y_test = sorted(glob('data/dsb2018/test/masks/*.tif'))\n",
    "assert all(Path(x).name==Path(y).name for x,y in zip(X,Y))\n",
    "\n",
    "X = list(map(imread,X))\n",
    "Y = list(map(imread,Y))\n",
    "X_test = list(map(imread,X_test))\n",
    "Y_test = list(map(imread,Y_test))\n",
    "\n",
    "print('There is a total of {} images for the training set and {} images for the testing.'.format(len(X), len(X_test)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of image & mask is displayed below. Note that the mask is representing each nuclei with a different colour :\n",
    "- if a pixel is not associated to a nuclei, its value is set to 0 = black\n",
    "- then, each nuclei is assigned a unique pixel value. This way, each object can be easily differentiated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 nuclei have been segmented on this image.\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 435,
       "width": 907
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualization of a single image and the associated mask\n",
    "\n",
    "i = min(4, len(X)-1)\n",
    "img, lbl = X[i], fill_label_holes(Y[i])\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "plt.subplot(121); plt.imshow(img,cmap='gray');   plt.axis('off'); plt.title('Raw image')\n",
    "plt.subplot(122); plt.imshow(lbl,cmap=lbl_cmap); plt.axis('off'); plt.title('GT labels')\n",
    "\n",
    "# Count the number of nuclei segmented in the image\n",
    "\n",
    "n_nuclei = len(np.unique(Y[i])) - 1\n",
    "print('{} nuclei have been segmented on this image.'.format(n_nuclei))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4- Reconstruction of nuclei shape with star-convex polygons\n",
    "\n",
    "StarDist is predicting the position and shape of every nuclei in the image. The shape of each nucleus is approximated by a star-convex polygon. The quality of the fit depends on the number of rays used to define the polygons. The higher the number of rays, the better the approximation. However, the computational time will increase with the number of rays.\n",
    "In the following example, nuclei are reconstructed by polygons with varying number of rays. The optimal representation is obtained for 32 rays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 787,
       "width": 1144
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from stardist import relabel_image_stardist\n",
    "\n",
    "n_rays = [2**i for i in range(2,8)]\n",
    "fig, ax = plt.subplots(2,3, figsize=(16,11))\n",
    "for a,r in zip(ax.flat,n_rays):\n",
    "    a.imshow(relabel_image_stardist(lbl, n_rays=r), cmap=lbl_cmap)\n",
    "    a.set_title('Reconstructed (%d rays)' % r)\n",
    "    a.axis('off')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5- Training of a StarDist model for nuclei segmentation\n",
    "\n",
    "In the following section, the parameters for the StarDist network are defined :\n",
    "- n_rays will define the number of rays used to reconstruct the shape of the nuclei\n",
    "- grid is indicating whether the images are used with their original size (1,1) or if a subsample is used. For example, a grid of (2,2) means that the size of each image will be reduced by a factor of 2 in X and Y. This operation can help accelerate the calculation without reducing the performances of the network.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using default values: prob_thresh=0.5, nms_thresh=0.4.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_model.py (148): output path for model already exists, files may be overwritten: /home/jb/Repositories/qBioMaster-introduction/constructs/UE_Introduction/models/stardist\n"
     ]
    }
   ],
   "source": [
    "from stardist import fill_label_holes, random_label_cmap, calculate_extents, gputools_available\n",
    "from stardist.matching import matching, matching_dataset\n",
    "from stardist.models import Config2D, StarDist2D, StarDistData2D\n",
    "\n",
    "n_rays = 32\n",
    "\n",
    "# Use OpenCL-based computations for data generator during training (requires 'gputools')\n",
    "use_gpu = True and gputools_available()\n",
    "\n",
    "# Predict on subsampled grid for increased efficiency and larger field of view\n",
    "grid = (2,2)\n",
    "\n",
    "conf = Config2D (\n",
    "    n_rays          = n_rays,\n",
    "    grid            = grid,\n",
    "    use_gpu         = use_gpu,\n",
    "    n_channel_in    = 1,\n",
    ")\n",
    "\n",
    "model = StarDist2D(conf, name='stardist', basedir='models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the imaging conditions depend on the sample and the microscope, there is an important variability in fluorescence intensity between images. Therefore, before launching the training, we need to perform a normalization operation on all images (https://en.wikipedia.org/wiki/Normalization_(image_processing). This operation is critical for generalization of the network.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 447/447 [00:00<00:00, 784.77it/s]\n",
      "100%|██████████| 447/447 [00:01<00:00, 234.61it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from csbdeep.utils import Path, normalize\n",
    "\n",
    "X = [normalize(x,1,99.8,axis=(0,1)) for x in tqdm(X)]\n",
    "Y = [fill_label_holes(y) for y in tqdm(Y)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the training, in order to limit the memory usage, the network is not going to work on large images but on small patches. The size of the patch, called receptive field or network field of view (https://theaisummer.com/receptive-field/), is defined by the architecutre of the network and parameters such as the grid size defined earlier. However, it is important to make sure that the network field of view is large enough to englobe large nuclei. <br>\n",
    "Below, the median size of the nuclei is calculated and compare to the receptive field of the network. If the receptive field is too small, the parameters need to be changed (for example by increasing the size of the grid)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median object size:      [17.5 18. ]\n",
      "network field of view :  [93 94]\n"
     ]
    }
   ],
   "source": [
    "median_size = calculate_extents(list(Y), np.median)\n",
    "fov = np.array(model._axes_tile_overlap('YX'))\n",
    "print(f\"median object size:      {median_size}\")\n",
    "print(f\"network field of view :  {fov}\")\n",
    "if any(median_size > fov):\n",
    "    print(\"WARNING: median object size larger than field of view of the neural network.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the training, the data X,Y are split in two :\n",
    "- the training data X_trn, Y_trn will be fed to the network in order to recognize the features associated to the nuclei\n",
    "- the validation data X_val, Y_val will be used during the training to monitor how the network is learning\n",
    "\n",
    "As a rule of thumbs, ~15/20% of the data are used for the validation. <br>\n",
    "For the example below, 200 epochs have been used. Note that the training could take more than 30 minutes before completion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images: 447\n",
      "- training:       380\n",
      "- validation:      67\n",
      "Epoch 1/200\n",
      "10/10 [==============================] - 10s 905ms/step - loss: 3.0720 - prob_loss: 0.6839 - dist_loss: 11.9405 - prob_kld: 0.6305 - dist_relevant_mae: 11.9405 - dist_relevant_mse: 255.5674 - val_loss: 2.9948 - val_prob_loss: 0.4455 - val_dist_loss: 12.7467 - val_prob_kld: 0.3766 - val_dist_relevant_mae: 12.7467 - val_dist_relevant_mse: 269.8879\n",
      "Epoch 2/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 2.6427 - prob_loss: 0.4607 - dist_loss: 10.9102 - prob_kld: 0.3865 - dist_relevant_mae: 10.9101 - dist_relevant_mse: 207.5135 - val_loss: 2.7090 - val_prob_loss: 0.2950 - val_dist_loss: 12.0702 - val_prob_kld: 0.2261 - val_dist_relevant_mae: 12.0702 - val_dist_relevant_mse: 252.4984\n",
      "Epoch 3/200\n",
      "10/10 [==============================] - 9s 911ms/step - loss: 3.1441 - prob_loss: 0.3789 - dist_loss: 13.8260 - prob_kld: 0.3089 - dist_relevant_mae: 13.8258 - dist_relevant_mse: 332.7113 - val_loss: 2.4462 - val_prob_loss: 0.2327 - val_dist_loss: 11.0673 - val_prob_kld: 0.1639 - val_dist_relevant_mae: 11.0671 - val_dist_relevant_mse: 227.6154\n",
      "Epoch 4/200\n",
      "10/10 [==============================] - 9s 897ms/step - loss: 2.7700 - prob_loss: 0.2596 - dist_loss: 12.5519 - prob_kld: 0.1853 - dist_relevant_mae: 12.5515 - dist_relevant_mse: 283.5963 - val_loss: 1.9496 - val_prob_loss: 0.2351 - val_dist_loss: 8.5726 - val_prob_kld: 0.1663 - val_dist_relevant_mae: 8.5717 - val_dist_relevant_mse: 137.2473\n",
      "Epoch 5/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 2.2833 - prob_loss: 0.3385 - dist_loss: 9.7239 - prob_kld: 0.2484 - dist_relevant_mae: 9.7231 - dist_relevant_mse: 186.2525 - val_loss: 1.7724 - val_prob_loss: 0.2465 - val_dist_loss: 7.6293 - val_prob_kld: 0.1777 - val_dist_relevant_mae: 7.6284 - val_dist_relevant_mse: 111.8169\n",
      "Epoch 6/200\n",
      "10/10 [==============================] - 9s 909ms/step - loss: 2.0699 - prob_loss: 0.3018 - dist_loss: 8.8400 - prob_kld: 0.2170 - dist_relevant_mae: 8.8391 - dist_relevant_mse: 149.8928 - val_loss: 1.6956 - val_prob_loss: 0.2568 - val_dist_loss: 7.1942 - val_prob_kld: 0.1879 - val_dist_relevant_mae: 7.1933 - val_dist_relevant_mse: 97.4793\n",
      "Epoch 7/200\n",
      "10/10 [==============================] - 9s 897ms/step - loss: 1.5197 - prob_loss: 0.2383 - dist_loss: 6.4072 - prob_kld: 0.1778 - dist_relevant_mae: 6.4065 - dist_relevant_mse: 82.9455 - val_loss: 1.7278 - val_prob_loss: 0.3624 - val_dist_loss: 6.8271 - val_prob_kld: 0.2935 - val_dist_relevant_mae: 6.8263 - val_dist_relevant_mse: 99.7940\n",
      "Epoch 8/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 1.8166 - prob_loss: 0.3134 - dist_loss: 7.5161 - prob_kld: 0.2396 - dist_relevant_mae: 7.5154 - dist_relevant_mse: 124.8760 - val_loss: 1.5834 - val_prob_loss: 0.2586 - val_dist_loss: 6.6239 - val_prob_kld: 0.1898 - val_dist_relevant_mae: 6.6231 - val_dist_relevant_mse: 84.3901\n",
      "Epoch 9/200\n",
      "10/10 [==============================] - 9s 913ms/step - loss: 1.8600 - prob_loss: 0.3034 - dist_loss: 7.7832 - prob_kld: 0.2181 - dist_relevant_mae: 7.7823 - dist_relevant_mse: 117.5820 - val_loss: 1.5370 - val_prob_loss: 0.2563 - val_dist_loss: 6.4034 - val_prob_kld: 0.1874 - val_dist_relevant_mae: 6.4026 - val_dist_relevant_mse: 90.0309\n",
      "Epoch 10/200\n",
      "10/10 [==============================] - 9s 901ms/step - loss: 1.6452 - prob_loss: 0.2759 - dist_loss: 6.8463 - prob_kld: 0.2019 - dist_relevant_mae: 6.8455 - dist_relevant_mse: 97.6823 - val_loss: 1.4696 - val_prob_loss: 0.2897 - val_dist_loss: 5.8997 - val_prob_kld: 0.2209 - val_dist_relevant_mae: 5.8989 - val_dist_relevant_mse: 69.9866\n",
      "Epoch 11/200\n",
      "10/10 [==============================] - 9s 896ms/step - loss: 1.6741 - prob_loss: 0.3183 - dist_loss: 6.7790 - prob_kld: 0.2440 - dist_relevant_mae: 6.7782 - dist_relevant_mse: 91.0842 - val_loss: 1.4102 - val_prob_loss: 0.2252 - val_dist_loss: 5.9251 - val_prob_kld: 0.1564 - val_dist_relevant_mae: 5.9241 - val_dist_relevant_mse: 70.6897\n",
      "Epoch 12/200\n",
      "10/10 [==============================] - 9s 898ms/step - loss: 1.5537 - prob_loss: 0.2760 - dist_loss: 6.3887 - prob_kld: 0.1922 - dist_relevant_mae: 6.3877 - dist_relevant_mse: 83.5684 - val_loss: 1.4918 - val_prob_loss: 0.2869 - val_dist_loss: 6.0247 - val_prob_kld: 0.2180 - val_dist_relevant_mae: 6.0241 - val_dist_relevant_mse: 79.2428\n",
      "Epoch 13/200\n",
      "10/10 [==============================] - 9s 903ms/step - loss: 1.8160 - prob_loss: 0.3273 - dist_loss: 7.4436 - prob_kld: 0.2479 - dist_relevant_mae: 7.4428 - dist_relevant_mse: 114.4384 - val_loss: 1.4864 - val_prob_loss: 0.2835 - val_dist_loss: 6.0145 - val_prob_kld: 0.2146 - val_dist_relevant_mae: 6.0134 - val_dist_relevant_mse: 65.7722\n",
      "Epoch 14/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 1.3959 - prob_loss: 0.2422 - dist_loss: 5.7682 - prob_kld: 0.1727 - dist_relevant_mae: 5.7673 - dist_relevant_mse: 68.4371 - val_loss: 1.4461 - val_prob_loss: 0.2999 - val_dist_loss: 5.7313 - val_prob_kld: 0.2310 - val_dist_relevant_mae: 5.7305 - val_dist_relevant_mse: 72.6839\n",
      "Epoch 15/200\n",
      "10/10 [==============================] - 9s 876ms/step - loss: 1.4645 - prob_loss: 0.2823 - dist_loss: 5.9110 - prob_kld: 0.2093 - dist_relevant_mae: 5.9101 - dist_relevant_mse: 73.8485 - val_loss: 1.3719 - val_prob_loss: 0.2422 - val_dist_loss: 5.6483 - val_prob_kld: 0.1734 - val_dist_relevant_mae: 5.6473 - val_dist_relevant_mse: 63.0657\n",
      "Epoch 16/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 1.3596 - prob_loss: 0.2254 - dist_loss: 5.6713 - prob_kld: 0.1676 - dist_relevant_mae: 5.6703 - dist_relevant_mse: 65.0926 - val_loss: 1.3903 - val_prob_loss: 0.2629 - val_dist_loss: 5.6374 - val_prob_kld: 0.1940 - val_dist_relevant_mae: 5.6365 - val_dist_relevant_mse: 61.4612\n",
      "Epoch 17/200\n",
      "10/10 [==============================] - 9s 922ms/step - loss: 1.4182 - prob_loss: 0.2506 - dist_loss: 5.8378 - prob_kld: 0.1806 - dist_relevant_mae: 5.8369 - dist_relevant_mse: 73.0513 - val_loss: 1.3545 - val_prob_loss: 0.2481 - val_dist_loss: 5.5319 - val_prob_kld: 0.1793 - val_dist_relevant_mae: 5.5309 - val_dist_relevant_mse: 60.7821\n",
      "Epoch 18/200\n",
      "10/10 [==============================] - 9s 916ms/step - loss: 1.5380 - prob_loss: 0.2557 - dist_loss: 6.4111 - prob_kld: 0.1584 - dist_relevant_mae: 6.4100 - dist_relevant_mse: 83.2452 - val_loss: 1.2803 - val_prob_loss: 0.1826 - val_dist_loss: 5.4889 - val_prob_kld: 0.1137 - val_dist_relevant_mae: 5.4877 - val_dist_relevant_mse: 60.4809\n",
      "Epoch 19/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 1.2179 - prob_loss: 0.1870 - dist_loss: 5.1546 - prob_kld: 0.1160 - dist_relevant_mae: 5.1536 - dist_relevant_mse: 53.9257 - val_loss: 1.2660 - val_prob_loss: 0.1950 - val_dist_loss: 5.3550 - val_prob_kld: 0.1262 - val_dist_relevant_mae: 5.3540 - val_dist_relevant_mse: 57.8984\n",
      "Epoch 20/200\n",
      "10/10 [==============================] - 9s 901ms/step - loss: 1.2630 - prob_loss: 0.2071 - dist_loss: 5.2795 - prob_kld: 0.1179 - dist_relevant_mae: 5.2785 - dist_relevant_mse: 55.1011 - val_loss: 1.4923 - val_prob_loss: 0.1536 - val_dist_loss: 6.6939 - val_prob_kld: 0.0847 - val_dist_relevant_mae: 6.6926 - val_dist_relevant_mse: 74.4877\n",
      "Epoch 21/200\n",
      "10/10 [==============================] - 9s 912ms/step - loss: 1.6027 - prob_loss: 0.1653 - dist_loss: 7.1868 - prob_kld: 0.0981 - dist_relevant_mae: 7.1857 - dist_relevant_mse: 100.3566 - val_loss: 1.1870 - val_prob_loss: 0.1505 - val_dist_loss: 5.1828 - val_prob_kld: 0.0816 - val_dist_relevant_mae: 5.1820 - val_dist_relevant_mse: 57.6355\n",
      "Epoch 22/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 1.2495 - prob_loss: 0.1616 - dist_loss: 5.4398 - prob_kld: 0.0947 - dist_relevant_mae: 5.4390 - dist_relevant_mse: 65.5338 - val_loss: 1.3875 - val_prob_loss: 0.3085 - val_dist_loss: 5.3951 - val_prob_kld: 0.2397 - val_dist_relevant_mae: 5.3944 - val_dist_relevant_mse: 66.5842\n",
      "Epoch 23/200\n",
      "10/10 [==============================] - 9s 895ms/step - loss: 1.1447 - prob_loss: 0.2369 - dist_loss: 4.5393 - prob_kld: 0.1755 - dist_relevant_mae: 4.5386 - dist_relevant_mse: 47.0265 - val_loss: 1.1599 - val_prob_loss: 0.1460 - val_dist_loss: 5.0694 - val_prob_kld: 0.0771 - val_dist_relevant_mae: 5.0686 - val_dist_relevant_mse: 56.0982\n",
      "Epoch 24/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 1.4365 - prob_loss: 0.1613 - dist_loss: 6.3761 - prob_kld: 0.0850 - dist_relevant_mae: 6.3753 - dist_relevant_mse: 89.2000 - val_loss: 1.0860 - val_prob_loss: 0.1479 - val_dist_loss: 4.6906 - val_prob_kld: 0.0791 - val_dist_relevant_mae: 4.6898 - val_dist_relevant_mse: 48.1619\n",
      "Epoch 25/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 1.2048 - prob_loss: 0.1113 - dist_loss: 5.4672 - prob_kld: 0.0455 - dist_relevant_mae: 5.4664 - dist_relevant_mse: 67.5621 - val_loss: 1.2565 - val_prob_loss: 0.1170 - val_dist_loss: 5.6975 - val_prob_kld: 0.0482 - val_dist_relevant_mae: 5.6970 - val_dist_relevant_mse: 74.7624\n",
      "Epoch 26/200\n",
      "10/10 [==============================] - 9s 912ms/step - loss: 1.2704 - prob_loss: 0.1384 - dist_loss: 5.6604 - prob_kld: 0.0599 - dist_relevant_mae: 5.6598 - dist_relevant_mse: 69.4395 - val_loss: 1.0589 - val_prob_loss: 0.1444 - val_dist_loss: 4.5726 - val_prob_kld: 0.0755 - val_dist_relevant_mae: 4.5719 - val_dist_relevant_mse: 46.5004\n",
      "Epoch 27/200\n",
      "10/10 [==============================] - 9s 922ms/step - loss: 1.1613 - prob_loss: 0.1445 - dist_loss: 5.0843 - prob_kld: 0.0697 - dist_relevant_mae: 5.0836 - dist_relevant_mse: 60.8679 - val_loss: 1.0589 - val_prob_loss: 0.1533 - val_dist_loss: 4.5279 - val_prob_kld: 0.0845 - val_dist_relevant_mae: 4.5272 - val_dist_relevant_mse: 41.8901\n",
      "Epoch 28/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 1.1841 - prob_loss: 0.1549 - dist_loss: 5.1464 - prob_kld: 0.0813 - dist_relevant_mae: 5.1456 - dist_relevant_mse: 59.5061 - val_loss: 0.9412 - val_prob_loss: 0.0953 - val_dist_loss: 4.2299 - val_prob_kld: 0.0264 - val_dist_relevant_mae: 4.2290 - val_dist_relevant_mse: 36.6898\n",
      "Epoch 29/200\n",
      "10/10 [==============================] - 9s 906ms/step - loss: 0.9209 - prob_loss: 0.0838 - dist_loss: 4.1856 - prob_kld: 0.0260 - dist_relevant_mae: 4.1848 - dist_relevant_mse: 39.4904 - val_loss: 0.8379 - val_prob_loss: 0.0987 - val_dist_loss: 3.6960 - val_prob_kld: 0.0298 - val_dist_relevant_mae: 3.6954 - val_dist_relevant_mse: 31.6440\n",
      "Epoch 30/200\n",
      "10/10 [==============================] - 9s 920ms/step - loss: 0.9032 - prob_loss: 0.1010 - dist_loss: 4.0108 - prob_kld: 0.0312 - dist_relevant_mae: 4.0101 - dist_relevant_mse: 37.6156 - val_loss: 0.9251 - val_prob_loss: 0.0973 - val_dist_loss: 4.1389 - val_prob_kld: 0.0284 - val_dist_relevant_mae: 4.1381 - val_dist_relevant_mse: 35.4967\n",
      "Epoch 31/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 1.0932 - prob_loss: 0.0950 - dist_loss: 4.9910 - prob_kld: 0.0278 - dist_relevant_mae: 4.9904 - dist_relevant_mse: 55.4218 - val_loss: 0.9934 - val_prob_loss: 0.1007 - val_dist_loss: 4.4637 - val_prob_kld: 0.0319 - val_dist_relevant_mae: 4.4631 - val_dist_relevant_mse: 41.4125\n",
      "Epoch 32/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 1.1689 - prob_loss: 0.1115 - dist_loss: 5.2873 - prob_kld: 0.0306 - dist_relevant_mae: 5.2867 - dist_relevant_mse: 60.8404 - val_loss: 0.8578 - val_prob_loss: 0.0897 - val_dist_loss: 3.8407 - val_prob_kld: 0.0208 - val_dist_relevant_mae: 3.8402 - val_dist_relevant_mse: 36.6362\n",
      "Epoch 33/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 1.1475 - prob_loss: 0.1148 - dist_loss: 5.1637 - prob_kld: 0.0241 - dist_relevant_mae: 5.1631 - dist_relevant_mse: 61.7635 - val_loss: 0.8443 - val_prob_loss: 0.0902 - val_dist_loss: 3.7705 - val_prob_kld: 0.0214 - val_dist_relevant_mae: 3.7700 - val_dist_relevant_mse: 37.3214\n",
      "Epoch 34/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.9675 - prob_loss: 0.1041 - dist_loss: 4.3169 - prob_kld: 0.0200 - dist_relevant_mae: 4.3163 - dist_relevant_mse: 46.3066 - val_loss: 0.7839 - val_prob_loss: 0.0941 - val_dist_loss: 3.4491 - val_prob_kld: 0.0253 - val_dist_relevant_mae: 3.4485 - val_dist_relevant_mse: 27.1485\n",
      "Epoch 35/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.9106 - prob_loss: 0.0993 - dist_loss: 4.0562 - prob_kld: 0.0250 - dist_relevant_mae: 4.0556 - dist_relevant_mse: 37.7411 - val_loss: 0.7958 - val_prob_loss: 0.0880 - val_dist_loss: 3.5390 - val_prob_kld: 0.0192 - val_dist_relevant_mae: 3.5384 - val_dist_relevant_mse: 34.0918\n",
      "Epoch 36/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.8392 - prob_loss: 0.1071 - dist_loss: 3.6606 - prob_kld: 0.0263 - dist_relevant_mae: 3.6599 - dist_relevant_mse: 32.6644 - val_loss: 0.7217 - val_prob_loss: 0.0866 - val_dist_loss: 3.1757 - val_prob_kld: 0.0177 - val_dist_relevant_mae: 3.1751 - val_dist_relevant_mse: 25.7036\n",
      "Epoch 37/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 0.9006 - prob_loss: 0.1136 - dist_loss: 3.9350 - prob_kld: 0.0308 - dist_relevant_mae: 3.9344 - dist_relevant_mse: 37.0810 - val_loss: 0.9012 - val_prob_loss: 0.0863 - val_dist_loss: 4.0746 - val_prob_kld: 0.0175 - val_dist_relevant_mae: 4.0740 - val_dist_relevant_mse: 32.6877\n",
      "Epoch 38/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.9634 - prob_loss: 0.0996 - dist_loss: 4.3191 - prob_kld: 0.0203 - dist_relevant_mae: 4.3185 - dist_relevant_mse: 42.6644 - val_loss: 0.7283 - val_prob_loss: 0.0866 - val_dist_loss: 3.2085 - val_prob_kld: 0.0177 - val_dist_relevant_mae: 3.2079 - val_dist_relevant_mse: 24.9023\n",
      "Epoch 39/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.8334 - prob_loss: 0.1087 - dist_loss: 3.6234 - prob_kld: 0.0219 - dist_relevant_mae: 3.6228 - dist_relevant_mse: 31.0128 - val_loss: 0.7042 - val_prob_loss: 0.0865 - val_dist_loss: 3.0881 - val_prob_kld: 0.0177 - val_dist_relevant_mae: 3.0876 - val_dist_relevant_mse: 25.0250\n",
      "Epoch 40/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.7549 - prob_loss: 0.0833 - dist_loss: 3.3577 - prob_kld: 0.0194 - dist_relevant_mae: 3.3571 - dist_relevant_mse: 28.9710 - val_loss: 0.8612 - val_prob_loss: 0.0896 - val_dist_loss: 3.8580 - val_prob_kld: 0.0208 - val_dist_relevant_mae: 3.8573 - val_dist_relevant_mse: 30.8751\n",
      "Epoch 41/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.8851 - prob_loss: 0.1078 - dist_loss: 3.8865 - prob_kld: 0.0254 - dist_relevant_mae: 3.8859 - dist_relevant_mse: 35.5143 - val_loss: 0.6858 - val_prob_loss: 0.0851 - val_dist_loss: 3.0036 - val_prob_kld: 0.0163 - val_dist_relevant_mae: 3.0031 - val_dist_relevant_mse: 23.5410\n",
      "Epoch 42/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.7942 - prob_loss: 0.0897 - dist_loss: 3.5226 - prob_kld: 0.0175 - dist_relevant_mae: 3.5220 - dist_relevant_mse: 31.2030 - val_loss: 0.7356 - val_prob_loss: 0.0852 - val_dist_loss: 3.2520 - val_prob_kld: 0.0164 - val_dist_relevant_mae: 3.2515 - val_dist_relevant_mse: 28.2855\n",
      "Epoch 43/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.7200 - prob_loss: 0.0921 - dist_loss: 3.1395 - prob_kld: 0.0204 - dist_relevant_mae: 3.1389 - dist_relevant_mse: 23.4126 - val_loss: 0.7312 - val_prob_loss: 0.0873 - val_dist_loss: 3.2196 - val_prob_kld: 0.0184 - val_dist_relevant_mae: 3.2191 - val_dist_relevant_mse: 25.7989\n",
      "Epoch 44/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.8367 - prob_loss: 0.0918 - dist_loss: 3.7246 - prob_kld: 0.0164 - dist_relevant_mae: 3.7241 - dist_relevant_mse: 36.3133 - val_loss: 0.9333 - val_prob_loss: 0.0848 - val_dist_loss: 4.2427 - val_prob_kld: 0.0160 - val_dist_relevant_mae: 4.2422 - val_dist_relevant_mse: 46.2225\n",
      "Epoch 45/200\n",
      "10/10 [==============================] - 9s 900ms/step - loss: 1.2243 - prob_loss: 0.1146 - dist_loss: 5.5487 - prob_kld: 0.0188 - dist_relevant_mae: 5.5482 - dist_relevant_mse: 75.1527 - val_loss: 0.9406 - val_prob_loss: 0.0853 - val_dist_loss: 4.2767 - val_prob_kld: 0.0165 - val_dist_relevant_mae: 4.2763 - val_dist_relevant_mse: 45.5486\n",
      "Epoch 46/200\n",
      "10/10 [==============================] - 9s 881ms/step - loss: 0.9946 - prob_loss: 0.0964 - dist_loss: 4.4906 - prob_kld: 0.0216 - dist_relevant_mae: 4.4901 - dist_relevant_mse: 49.1757 - val_loss: 0.8441 - val_prob_loss: 0.0860 - val_dist_loss: 3.7909 - val_prob_kld: 0.0171 - val_dist_relevant_mae: 3.7905 - val_dist_relevant_mse: 37.1931\n",
      "Epoch 47/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 1.0120 - prob_loss: 0.1050 - dist_loss: 4.5349 - prob_kld: 0.0232 - dist_relevant_mae: 4.5344 - dist_relevant_mse: 49.2855 - val_loss: 0.6744 - val_prob_loss: 0.0843 - val_dist_loss: 2.9506 - val_prob_kld: 0.0154 - val_dist_relevant_mae: 2.9500 - val_dist_relevant_mse: 23.7875\n",
      "Epoch 48/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.7657 - prob_loss: 0.0929 - dist_loss: 3.3640 - prob_kld: 0.0156 - dist_relevant_mae: 3.3633 - dist_relevant_mse: 28.6850 - val_loss: 0.6850 - val_prob_loss: 0.0840 - val_dist_loss: 3.0052 - val_prob_kld: 0.0152 - val_dist_relevant_mae: 3.0045 - val_dist_relevant_mse: 24.5448\n",
      "Epoch 49/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.9262 - prob_loss: 0.1159 - dist_loss: 4.0516 - prob_kld: 0.0218 - dist_relevant_mae: 4.0509 - dist_relevant_mse: 42.1596 - val_loss: 0.6880 - val_prob_loss: 0.0838 - val_dist_loss: 3.0214 - val_prob_kld: 0.0149 - val_dist_relevant_mae: 3.0208 - val_dist_relevant_mse: 21.2665\n",
      "Epoch 50/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.7595 - prob_loss: 0.0779 - dist_loss: 3.4078 - prob_kld: 0.0156 - dist_relevant_mae: 3.4072 - dist_relevant_mse: 32.8326 - val_loss: 0.6703 - val_prob_loss: 0.0835 - val_dist_loss: 2.9340 - val_prob_kld: 0.0147 - val_dist_relevant_mae: 2.9334 - val_dist_relevant_mse: 21.7216\n",
      "Epoch 51/200\n",
      "10/10 [==============================] - 9s 900ms/step - loss: 0.6647 - prob_loss: 0.0838 - dist_loss: 2.9042 - prob_kld: 0.0160 - dist_relevant_mae: 2.9036 - dist_relevant_mse: 21.3479 - val_loss: 0.6637 - val_prob_loss: 0.0843 - val_dist_loss: 2.8968 - val_prob_kld: 0.0155 - val_dist_relevant_mae: 2.8962 - val_dist_relevant_mse: 20.4348\n",
      "Epoch 52/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.8397 - prob_loss: 0.1072 - dist_loss: 3.6625 - prob_kld: 0.0223 - dist_relevant_mae: 3.6619 - dist_relevant_mse: 32.0893 - val_loss: 0.6692 - val_prob_loss: 0.0849 - val_dist_loss: 2.9217 - val_prob_kld: 0.0160 - val_dist_relevant_mae: 2.9211 - val_dist_relevant_mse: 21.3782\n",
      "Epoch 53/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.7121 - prob_loss: 0.1025 - dist_loss: 3.0482 - prob_kld: 0.0259 - dist_relevant_mae: 3.0476 - dist_relevant_mse: 23.8300 - val_loss: 0.7178 - val_prob_loss: 0.0828 - val_dist_loss: 3.1748 - val_prob_kld: 0.0139 - val_dist_relevant_mae: 3.1744 - val_dist_relevant_mse: 25.6434\n",
      "Epoch 54/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.7958 - prob_loss: 0.1008 - dist_loss: 3.4749 - prob_kld: 0.0239 - dist_relevant_mae: 3.4744 - dist_relevant_mse: 28.5123 - val_loss: 0.6575 - val_prob_loss: 0.0856 - val_dist_loss: 2.8595 - val_prob_kld: 0.0168 - val_dist_relevant_mae: 2.8590 - val_dist_relevant_mse: 22.7926\n",
      "Epoch 55/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.7881 - prob_loss: 0.0993 - dist_loss: 3.4439 - prob_kld: 0.0215 - dist_relevant_mae: 3.4433 - dist_relevant_mse: 30.1588 - val_loss: 0.6632 - val_prob_loss: 0.0903 - val_dist_loss: 2.8641 - val_prob_kld: 0.0215 - val_dist_relevant_mae: 2.8636 - val_dist_relevant_mse: 22.8921\n",
      "Epoch 56/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.7291 - prob_loss: 0.0862 - dist_loss: 3.2146 - prob_kld: 0.0197 - dist_relevant_mae: 3.2141 - dist_relevant_mse: 28.8106 - val_loss: 0.7076 - val_prob_loss: 0.0866 - val_dist_loss: 3.1052 - val_prob_kld: 0.0177 - val_dist_relevant_mae: 3.1047 - val_dist_relevant_mse: 22.7244\n",
      "Epoch 57/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.8859 - prob_loss: 0.1135 - dist_loss: 3.8619 - prob_kld: 0.0240 - dist_relevant_mae: 3.8614 - dist_relevant_mse: 40.6382 - val_loss: 0.6369 - val_prob_loss: 0.0905 - val_dist_loss: 2.7318 - val_prob_kld: 0.0217 - val_dist_relevant_mae: 2.7313 - val_dist_relevant_mse: 19.9961\n",
      "Epoch 58/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.7778 - prob_loss: 0.1009 - dist_loss: 3.3843 - prob_kld: 0.0236 - dist_relevant_mae: 3.3837 - dist_relevant_mse: 31.5802 - val_loss: 0.6850 - val_prob_loss: 0.0888 - val_dist_loss: 2.9814 - val_prob_kld: 0.0199 - val_dist_relevant_mae: 2.9809 - val_dist_relevant_mse: 26.1870\n",
      "Epoch 59/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.8643 - prob_loss: 0.1140 - dist_loss: 3.7517 - prob_kld: 0.0293 - dist_relevant_mae: 3.7511 - dist_relevant_mse: 36.8759 - val_loss: 0.6263 - val_prob_loss: 0.0830 - val_dist_loss: 2.7164 - val_prob_kld: 0.0142 - val_dist_relevant_mae: 2.7159 - val_dist_relevant_mse: 19.7874\n",
      "Epoch 60/200\n",
      "10/10 [==============================] - 9s 898ms/step - loss: 0.7007 - prob_loss: 0.0885 - dist_loss: 3.0606 - prob_kld: 0.0178 - dist_relevant_mae: 3.0600 - dist_relevant_mse: 23.5877 - val_loss: 0.6504 - val_prob_loss: 0.0834 - val_dist_loss: 2.8349 - val_prob_kld: 0.0145 - val_dist_relevant_mae: 2.8344 - val_dist_relevant_mse: 19.5754\n",
      "Epoch 61/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 0.7136 - prob_loss: 0.0748 - dist_loss: 3.1936 - prob_kld: 0.0129 - dist_relevant_mae: 3.1931 - dist_relevant_mse: 28.0700 - val_loss: 0.7171 - val_prob_loss: 0.0828 - val_dist_loss: 3.1714 - val_prob_kld: 0.0139 - val_dist_relevant_mae: 3.1708 - val_dist_relevant_mse: 22.1940\n",
      "Epoch 62/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 0.7729 - prob_loss: 0.1015 - dist_loss: 3.3568 - prob_kld: 0.0197 - dist_relevant_mae: 3.3563 - dist_relevant_mse: 26.3178 - val_loss: 0.7078 - val_prob_loss: 0.0829 - val_dist_loss: 3.1244 - val_prob_kld: 0.0141 - val_dist_relevant_mae: 3.1238 - val_dist_relevant_mse: 20.9564\n",
      "Epoch 63/200\n",
      "10/10 [==============================] - 9s 879ms/step - loss: 0.8515 - prob_loss: 0.0990 - dist_loss: 3.7624 - prob_kld: 0.0288 - dist_relevant_mae: 3.7618 - dist_relevant_mse: 34.6489 - val_loss: 0.6629 - val_prob_loss: 0.0845 - val_dist_loss: 2.8919 - val_prob_kld: 0.0156 - val_dist_relevant_mae: 2.8914 - val_dist_relevant_mse: 20.0669\n",
      "Epoch 64/200\n",
      "10/10 [==============================] - 9s 898ms/step - loss: 0.7520 - prob_loss: 0.0650 - dist_loss: 3.4351 - prob_kld: 0.0138 - dist_relevant_mae: 3.4346 - dist_relevant_mse: 32.7691 - val_loss: 0.8036 - val_prob_loss: 0.0859 - val_dist_loss: 3.5884 - val_prob_kld: 0.0171 - val_dist_relevant_mae: 3.5880 - val_dist_relevant_mse: 27.9262\n",
      "Epoch 65/200\n",
      "10/10 [==============================] - 9s 879ms/step - loss: 0.7826 - prob_loss: 0.0824 - dist_loss: 3.5012 - prob_kld: 0.0178 - dist_relevant_mae: 3.5006 - dist_relevant_mse: 31.4122 - val_loss: 0.6698 - val_prob_loss: 0.0845 - val_dist_loss: 2.9263 - val_prob_kld: 0.0157 - val_dist_relevant_mae: 2.9258 - val_dist_relevant_mse: 24.4494\n",
      "Epoch 66/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.7996 - prob_loss: 0.1148 - dist_loss: 3.4239 - prob_kld: 0.0258 - dist_relevant_mae: 3.4233 - dist_relevant_mse: 30.2357 - val_loss: 0.6370 - val_prob_loss: 0.0934 - val_dist_loss: 2.7180 - val_prob_kld: 0.0246 - val_dist_relevant_mae: 2.7174 - val_dist_relevant_mse: 19.9881\n",
      "Epoch 67/200\n",
      "10/10 [==============================] - 9s 897ms/step - loss: 0.5590 - prob_loss: 0.0749 - dist_loss: 2.4206 - prob_kld: 0.0191 - dist_relevant_mae: 2.4202 - dist_relevant_mse: 15.9206 - val_loss: 0.6297 - val_prob_loss: 0.0819 - val_dist_loss: 2.7393 - val_prob_kld: 0.0130 - val_dist_relevant_mae: 2.7388 - val_dist_relevant_mse: 21.6078\n",
      "Epoch 68/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.5944 - prob_loss: 0.0765 - dist_loss: 2.5892 - prob_kld: 0.0148 - dist_relevant_mae: 2.5888 - dist_relevant_mse: 19.5837 - val_loss: 0.6202 - val_prob_loss: 0.0824 - val_dist_loss: 2.6889 - val_prob_kld: 0.0136 - val_dist_relevant_mae: 2.6884 - val_dist_relevant_mse: 18.7188\n",
      "Epoch 69/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6779 - prob_loss: 0.1089 - dist_loss: 2.8451 - prob_kld: 0.0210 - dist_relevant_mae: 2.8446 - dist_relevant_mse: 20.2848 - val_loss: 0.6275 - val_prob_loss: 0.0827 - val_dist_loss: 2.7241 - val_prob_kld: 0.0138 - val_dist_relevant_mae: 2.7235 - val_dist_relevant_mse: 19.8454\n",
      "Epoch 70/200\n",
      "10/10 [==============================] - 9s 892ms/step - loss: 0.8360 - prob_loss: 0.1122 - dist_loss: 3.6190 - prob_kld: 0.0166 - dist_relevant_mae: 3.6184 - dist_relevant_mse: 35.7531 - val_loss: 0.7197 - val_prob_loss: 0.0826 - val_dist_loss: 3.1854 - val_prob_kld: 0.0138 - val_dist_relevant_mae: 3.1850 - val_dist_relevant_mse: 26.7249\n",
      "Epoch 71/200\n",
      "10/10 [==============================] - 9s 881ms/step - loss: 0.8509 - prob_loss: 0.1001 - dist_loss: 3.7542 - prob_kld: 0.0161 - dist_relevant_mae: 3.7537 - dist_relevant_mse: 37.9999 - val_loss: 0.7232 - val_prob_loss: 0.0844 - val_dist_loss: 3.1936 - val_prob_kld: 0.0156 - val_dist_relevant_mae: 3.1932 - val_dist_relevant_mse: 25.5294\n",
      "Epoch 72/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.5892 - prob_loss: 0.0679 - dist_loss: 2.6066 - prob_kld: 0.0132 - dist_relevant_mae: 2.6061 - dist_relevant_mse: 18.2065 - val_loss: 0.6325 - val_prob_loss: 0.0824 - val_dist_loss: 2.7503 - val_prob_kld: 0.0136 - val_dist_relevant_mae: 2.7498 - val_dist_relevant_mse: 20.9827\n",
      "Epoch 73/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.7658 - prob_loss: 0.0911 - dist_loss: 3.3734 - prob_kld: 0.0238 - dist_relevant_mae: 3.3728 - dist_relevant_mse: 31.7305 - val_loss: 0.6266 - val_prob_loss: 0.0900 - val_dist_loss: 2.6828 - val_prob_kld: 0.0212 - val_dist_relevant_mae: 2.6823 - val_dist_relevant_mse: 18.7480\n",
      "Epoch 74/200\n",
      "10/10 [==============================] - 9s 881ms/step - loss: 0.6392 - prob_loss: 0.0965 - dist_loss: 2.7134 - prob_kld: 0.0240 - dist_relevant_mae: 2.7129 - dist_relevant_mse: 18.8445 - val_loss: 0.6275 - val_prob_loss: 0.1057 - val_dist_loss: 2.6089 - val_prob_kld: 0.0369 - val_dist_relevant_mae: 2.6084 - val_dist_relevant_mse: 18.1715\n",
      "Epoch 75/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.7383 - prob_loss: 0.1115 - dist_loss: 3.1342 - prob_kld: 0.0373 - dist_relevant_mae: 3.1337 - dist_relevant_mse: 28.6647 - val_loss: 0.6211 - val_prob_loss: 0.0826 - val_dist_loss: 2.6928 - val_prob_kld: 0.0137 - val_dist_relevant_mae: 2.6923 - val_dist_relevant_mse: 21.3354\n",
      "Epoch 76/200\n",
      "10/10 [==============================] - 9s 904ms/step - loss: 0.6741 - prob_loss: 0.0961 - dist_loss: 2.8897 - prob_kld: 0.0222 - dist_relevant_mae: 2.8892 - dist_relevant_mse: 25.2930 - val_loss: 0.6104 - val_prob_loss: 0.0823 - val_dist_loss: 2.6402 - val_prob_kld: 0.0135 - val_dist_relevant_mae: 2.6397 - val_dist_relevant_mse: 19.3540\n",
      "Epoch 77/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.7148 - prob_loss: 0.1050 - dist_loss: 3.0488 - prob_kld: 0.0186 - dist_relevant_mae: 3.0482 - dist_relevant_mse: 24.7552 - val_loss: 0.6087 - val_prob_loss: 0.0838 - val_dist_loss: 2.6249 - val_prob_kld: 0.0149 - val_dist_relevant_mae: 2.6244 - val_dist_relevant_mse: 20.0772\n",
      "Epoch 78/200\n",
      "10/10 [==============================] - 9s 901ms/step - loss: 0.6750 - prob_loss: 0.1029 - dist_loss: 2.8608 - prob_kld: 0.0190 - dist_relevant_mae: 2.8603 - dist_relevant_mse: 23.4096 - val_loss: 0.6146 - val_prob_loss: 0.0829 - val_dist_loss: 2.6583 - val_prob_kld: 0.0141 - val_dist_relevant_mae: 2.6578 - val_dist_relevant_mse: 20.2150\n",
      "Epoch 79/200\n",
      "10/10 [==============================] - 9s 896ms/step - loss: 0.6881 - prob_loss: 0.0861 - dist_loss: 3.0103 - prob_kld: 0.0194 - dist_relevant_mae: 3.0098 - dist_relevant_mse: 23.0084 - val_loss: 0.6151 - val_prob_loss: 0.0870 - val_dist_loss: 2.6404 - val_prob_kld: 0.0182 - val_dist_relevant_mae: 2.6399 - val_dist_relevant_mse: 20.6024\n",
      "Epoch 80/200\n",
      "10/10 [==============================] - 9s 898ms/step - loss: 0.6767 - prob_loss: 0.0837 - dist_loss: 2.9649 - prob_kld: 0.0209 - dist_relevant_mae: 2.9644 - dist_relevant_mse: 24.1215 - val_loss: 0.6001 - val_prob_loss: 0.0831 - val_dist_loss: 2.5848 - val_prob_kld: 0.0143 - val_dist_relevant_mae: 2.5843 - val_dist_relevant_mse: 19.6409\n",
      "Epoch 81/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.6983 - prob_loss: 0.0852 - dist_loss: 3.0655 - prob_kld: 0.0205 - dist_relevant_mae: 3.0650 - dist_relevant_mse: 26.5978 - val_loss: 0.6591 - val_prob_loss: 0.0814 - val_dist_loss: 2.8886 - val_prob_kld: 0.0126 - val_dist_relevant_mae: 2.8881 - val_dist_relevant_mse: 24.9651\n",
      "Epoch 82/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.6268 - prob_loss: 0.0812 - dist_loss: 2.7279 - prob_kld: 0.0107 - dist_relevant_mae: 2.7275 - dist_relevant_mse: 22.6904 - val_loss: 0.6889 - val_prob_loss: 0.0814 - val_dist_loss: 3.0377 - val_prob_kld: 0.0125 - val_dist_relevant_mae: 3.0372 - val_dist_relevant_mse: 20.3237\n",
      "Epoch 83/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.7284 - prob_loss: 0.0962 - dist_loss: 3.1614 - prob_kld: 0.0136 - dist_relevant_mae: 3.1609 - dist_relevant_mse: 29.3775 - val_loss: 0.5933 - val_prob_loss: 0.0811 - val_dist_loss: 2.5607 - val_prob_kld: 0.0123 - val_dist_relevant_mae: 2.5602 - val_dist_relevant_mse: 17.9370\n",
      "Epoch 84/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.7294 - prob_loss: 0.0923 - dist_loss: 3.1857 - prob_kld: 0.0182 - dist_relevant_mae: 3.1851 - dist_relevant_mse: 29.5732 - val_loss: 0.5942 - val_prob_loss: 0.0835 - val_dist_loss: 2.5534 - val_prob_kld: 0.0146 - val_dist_relevant_mae: 2.5528 - val_dist_relevant_mse: 18.3189\n",
      "Epoch 85/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6684 - prob_loss: 0.0961 - dist_loss: 2.8616 - prob_kld: 0.0183 - dist_relevant_mae: 2.8610 - dist_relevant_mse: 23.6757 - val_loss: 0.6017 - val_prob_loss: 0.0855 - val_dist_loss: 2.5806 - val_prob_kld: 0.0167 - val_dist_relevant_mae: 2.5800 - val_dist_relevant_mse: 18.3828\n",
      "Epoch 86/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6897 - prob_loss: 0.0931 - dist_loss: 2.9831 - prob_kld: 0.0211 - dist_relevant_mae: 2.9825 - dist_relevant_mse: 23.5449 - val_loss: 0.5907 - val_prob_loss: 0.0821 - val_dist_loss: 2.5434 - val_prob_kld: 0.0132 - val_dist_relevant_mae: 2.5428 - val_dist_relevant_mse: 17.4610\n",
      "Epoch 87/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6785 - prob_loss: 0.0929 - dist_loss: 2.9275 - prob_kld: 0.0136 - dist_relevant_mae: 2.9270 - dist_relevant_mse: 23.3555 - val_loss: 0.6024 - val_prob_loss: 0.0813 - val_dist_loss: 2.6057 - val_prob_kld: 0.0125 - val_dist_relevant_mae: 2.6051 - val_dist_relevant_mse: 17.8943\n",
      "Epoch 88/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.7232 - prob_loss: 0.0880 - dist_loss: 3.1760 - prob_kld: 0.0127 - dist_relevant_mae: 3.1754 - dist_relevant_mse: 29.7333 - val_loss: 0.5884 - val_prob_loss: 0.0820 - val_dist_loss: 2.5317 - val_prob_kld: 0.0132 - val_dist_relevant_mae: 2.5313 - val_dist_relevant_mse: 17.2615\n",
      "Epoch 89/200\n",
      "10/10 [==============================] - 9s 892ms/step - loss: 0.6796 - prob_loss: 0.0966 - dist_loss: 2.9148 - prob_kld: 0.0180 - dist_relevant_mae: 2.9143 - dist_relevant_mse: 21.9575 - val_loss: 0.5897 - val_prob_loss: 0.0814 - val_dist_loss: 2.5414 - val_prob_kld: 0.0125 - val_dist_relevant_mae: 2.5409 - val_dist_relevant_mse: 17.8068\n",
      "Epoch 90/200\n",
      "10/10 [==============================] - 9s 892ms/step - loss: 0.6475 - prob_loss: 0.0790 - dist_loss: 2.8427 - prob_kld: 0.0178 - dist_relevant_mae: 2.8423 - dist_relevant_mse: 23.0234 - val_loss: 0.6147 - val_prob_loss: 0.0818 - val_dist_loss: 2.6648 - val_prob_kld: 0.0129 - val_dist_relevant_mae: 2.6643 - val_dist_relevant_mse: 17.7061\n",
      "Epoch 91/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.6596 - prob_loss: 0.0813 - dist_loss: 2.8916 - prob_kld: 0.0152 - dist_relevant_mae: 2.8911 - dist_relevant_mse: 24.4733 - val_loss: 0.5935 - val_prob_loss: 0.0817 - val_dist_loss: 2.5591 - val_prob_kld: 0.0129 - val_dist_relevant_mae: 2.5586 - val_dist_relevant_mse: 17.6776\n",
      "Epoch 92/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.6274 - prob_loss: 0.0939 - dist_loss: 2.6672 - prob_kld: 0.0138 - dist_relevant_mae: 2.6668 - dist_relevant_mse: 20.1177 - val_loss: 0.5808 - val_prob_loss: 0.0822 - val_dist_loss: 2.4932 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.4927 - val_dist_relevant_mse: 17.6363\n",
      "Epoch 93/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.7541 - prob_loss: 0.1111 - dist_loss: 3.2149 - prob_kld: 0.0180 - dist_relevant_mae: 3.2144 - dist_relevant_mse: 26.8038 - val_loss: 0.5915 - val_prob_loss: 0.0819 - val_dist_loss: 2.5481 - val_prob_kld: 0.0130 - val_dist_relevant_mae: 2.5476 - val_dist_relevant_mse: 17.1373\n",
      "Epoch 94/200\n",
      "10/10 [==============================] - 9s 900ms/step - loss: 0.7386 - prob_loss: 0.0969 - dist_loss: 3.2084 - prob_kld: 0.0180 - dist_relevant_mae: 3.2079 - dist_relevant_mse: 28.5361 - val_loss: 0.6077 - val_prob_loss: 0.0822 - val_dist_loss: 2.6273 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.6269 - val_dist_relevant_mse: 19.3066\n",
      "Epoch 95/200\n",
      "10/10 [==============================] - 9s 880ms/step - loss: 0.6069 - prob_loss: 0.0775 - dist_loss: 2.6471 - prob_kld: 0.0125 - dist_relevant_mae: 2.6466 - dist_relevant_mse: 17.5202 - val_loss: 0.5883 - val_prob_loss: 0.0810 - val_dist_loss: 2.5368 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.5363 - val_dist_relevant_mse: 19.3603\n",
      "Epoch 96/200\n",
      "10/10 [==============================] - 9s 878ms/step - loss: 0.7635 - prob_loss: 0.0761 - dist_loss: 3.4370 - prob_kld: 0.0151 - dist_relevant_mae: 3.4366 - dist_relevant_mse: 45.9835 - val_loss: 0.6188 - val_prob_loss: 0.0811 - val_dist_loss: 2.6887 - val_prob_kld: 0.0122 - val_dist_relevant_mae: 2.6882 - val_dist_relevant_mse: 22.3861\n",
      "Epoch 97/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6488 - prob_loss: 0.0798 - dist_loss: 2.8450 - prob_kld: 0.0159 - dist_relevant_mae: 2.8444 - dist_relevant_mse: 24.0374 - val_loss: 0.6226 - val_prob_loss: 0.0816 - val_dist_loss: 2.7051 - val_prob_kld: 0.0127 - val_dist_relevant_mae: 2.7046 - val_dist_relevant_mse: 22.0119\n",
      "Epoch 98/200\n",
      "10/10 [==============================] - 9s 895ms/step - loss: 0.6391 - prob_loss: 0.0796 - dist_loss: 2.7972 - prob_kld: 0.0154 - dist_relevant_mae: 2.7966 - dist_relevant_mse: 19.6298 - val_loss: 0.5795 - val_prob_loss: 0.0814 - val_dist_loss: 2.4906 - val_prob_kld: 0.0125 - val_dist_relevant_mae: 2.4901 - val_dist_relevant_mse: 18.4463\n",
      "Epoch 99/200\n",
      "10/10 [==============================] - 9s 924ms/step - loss: 0.6962 - prob_loss: 0.0905 - dist_loss: 3.0288 - prob_kld: 0.0167 - dist_relevant_mae: 3.0282 - dist_relevant_mse: 26.2912 - val_loss: 0.5938 - val_prob_loss: 0.0825 - val_dist_loss: 2.5569 - val_prob_kld: 0.0136 - val_dist_relevant_mae: 2.5564 - val_dist_relevant_mse: 17.1327\n",
      "Epoch 100/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6577 - prob_loss: 0.0937 - dist_loss: 2.8200 - prob_kld: 0.0164 - dist_relevant_mae: 2.8194 - dist_relevant_mse: 22.9147 - val_loss: 0.5745 - val_prob_loss: 0.0829 - val_dist_loss: 2.4580 - val_prob_kld: 0.0141 - val_dist_relevant_mae: 2.4575 - val_dist_relevant_mse: 16.7760\n",
      "Epoch 101/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.6875 - prob_loss: 0.0970 - dist_loss: 2.9523 - prob_kld: 0.0188 - dist_relevant_mae: 2.9517 - dist_relevant_mse: 25.0152 - val_loss: 0.6222 - val_prob_loss: 0.0820 - val_dist_loss: 2.7011 - val_prob_kld: 0.0131 - val_dist_relevant_mae: 2.7005 - val_dist_relevant_mse: 18.2442\n",
      "Epoch 102/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.5788 - prob_loss: 0.0762 - dist_loss: 2.5133 - prob_kld: 0.0117 - dist_relevant_mae: 2.5128 - dist_relevant_mse: 17.1611 - val_loss: 0.6806 - val_prob_loss: 0.0838 - val_dist_loss: 2.9840 - val_prob_kld: 0.0150 - val_dist_relevant_mae: 2.9834 - val_dist_relevant_mse: 19.5070\n",
      "Epoch 103/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.8358 - prob_loss: 0.1113 - dist_loss: 3.6226 - prob_kld: 0.0244 - dist_relevant_mae: 3.6220 - dist_relevant_mse: 35.6382 - val_loss: 0.6075 - val_prob_loss: 0.0864 - val_dist_loss: 2.6054 - val_prob_kld: 0.0176 - val_dist_relevant_mae: 2.6049 - val_dist_relevant_mse: 17.4170\n",
      "Epoch 104/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.7304 - prob_loss: 0.1063 - dist_loss: 3.1205 - prob_kld: 0.0232 - dist_relevant_mae: 3.1200 - dist_relevant_mse: 25.0846 - val_loss: 0.7402 - val_prob_loss: 0.0824 - val_dist_loss: 3.2891 - val_prob_kld: 0.0136 - val_dist_relevant_mae: 3.2885 - val_dist_relevant_mse: 21.0595\n",
      "Epoch 105/200\n",
      "10/10 [==============================] - 9s 880ms/step - loss: 0.8357 - prob_loss: 0.0947 - dist_loss: 3.7049 - prob_kld: 0.0159 - dist_relevant_mae: 3.7042 - dist_relevant_mse: 30.4931 - val_loss: 0.6262 - val_prob_loss: 0.0828 - val_dist_loss: 2.7170 - val_prob_kld: 0.0140 - val_dist_relevant_mae: 2.7163 - val_dist_relevant_mse: 18.8935\n",
      "Epoch 106/200\n",
      "10/10 [==============================] - 9s 892ms/step - loss: 0.7230 - prob_loss: 0.0949 - dist_loss: 3.1403 - prob_kld: 0.0173 - dist_relevant_mae: 3.1397 - dist_relevant_mse: 28.0748 - val_loss: 0.5909 - val_prob_loss: 0.0820 - val_dist_loss: 2.5448 - val_prob_kld: 0.0131 - val_dist_relevant_mae: 2.5442 - val_dist_relevant_mse: 17.1373\n",
      "Epoch 107/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.6992 - prob_loss: 0.0778 - dist_loss: 3.1070 - prob_kld: 0.0185 - dist_relevant_mae: 3.1063 - dist_relevant_mse: 31.4387 - val_loss: 0.5830 - val_prob_loss: 0.0826 - val_dist_loss: 2.5024 - val_prob_kld: 0.0137 - val_dist_relevant_mae: 2.5017 - val_dist_relevant_mse: 17.2744\n",
      "Epoch 108/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6031 - prob_loss: 0.0860 - dist_loss: 2.5855 - prob_kld: 0.0152 - dist_relevant_mae: 2.5847 - dist_relevant_mse: 18.2581 - val_loss: 0.6187 - val_prob_loss: 0.0806 - val_dist_loss: 2.6905 - val_prob_kld: 0.0117 - val_dist_relevant_mae: 2.6898 - val_dist_relevant_mse: 21.9381\n",
      "Epoch 109/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6756 - prob_loss: 0.0929 - dist_loss: 2.9138 - prob_kld: 0.0165 - dist_relevant_mae: 2.9132 - dist_relevant_mse: 23.9079 - val_loss: 0.5852 - val_prob_loss: 0.0824 - val_dist_loss: 2.5142 - val_prob_kld: 0.0135 - val_dist_relevant_mae: 2.5137 - val_dist_relevant_mse: 18.8938\n",
      "Epoch 110/200\n",
      "10/10 [==============================] - 9s 892ms/step - loss: 0.7053 - prob_loss: 0.1079 - dist_loss: 2.9873 - prob_kld: 0.0181 - dist_relevant_mae: 2.9867 - dist_relevant_mse: 23.5298 - val_loss: 0.5634 - val_prob_loss: 0.0804 - val_dist_loss: 2.4147 - val_prob_kld: 0.0116 - val_dist_relevant_mae: 2.4142 - val_dist_relevant_mse: 16.4896\n",
      "Epoch 111/200\n",
      "10/10 [==============================] - 9s 878ms/step - loss: 0.5986 - prob_loss: 0.0774 - dist_loss: 2.6056 - prob_kld: 0.0156 - dist_relevant_mae: 2.6051 - dist_relevant_mse: 19.3671 - val_loss: 0.5560 - val_prob_loss: 0.0856 - val_dist_loss: 2.3522 - val_prob_kld: 0.0167 - val_dist_relevant_mae: 2.3517 - val_dist_relevant_mse: 15.8231\n",
      "Epoch 112/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.6172 - prob_loss: 0.0907 - dist_loss: 2.6322 - prob_kld: 0.0158 - dist_relevant_mae: 2.6317 - dist_relevant_mse: 19.4633 - val_loss: 0.5740 - val_prob_loss: 0.0809 - val_dist_loss: 2.4656 - val_prob_kld: 0.0120 - val_dist_relevant_mae: 2.4651 - val_dist_relevant_mse: 18.6009\n",
      "Epoch 113/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 0.5238 - prob_loss: 0.0810 - dist_loss: 2.2142 - prob_kld: 0.0139 - dist_relevant_mae: 2.2137 - dist_relevant_mse: 13.5072 - val_loss: 0.5499 - val_prob_loss: 0.0808 - val_dist_loss: 2.3458 - val_prob_kld: 0.0119 - val_dist_relevant_mae: 2.3452 - val_dist_relevant_mse: 16.1708\n",
      "Epoch 114/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.7079 - prob_loss: 0.0899 - dist_loss: 3.0904 - prob_kld: 0.0119 - dist_relevant_mae: 3.0898 - dist_relevant_mse: 28.3310 - val_loss: 0.5730 - val_prob_loss: 0.0806 - val_dist_loss: 2.4621 - val_prob_kld: 0.0117 - val_dist_relevant_mae: 2.4616 - val_dist_relevant_mse: 16.1398\n",
      "Epoch 115/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.5752 - prob_loss: 0.0762 - dist_loss: 2.4949 - prob_kld: 0.0133 - dist_relevant_mae: 2.4943 - dist_relevant_mse: 19.1873 - val_loss: 0.5847 - val_prob_loss: 0.0807 - val_dist_loss: 2.5200 - val_prob_kld: 0.0119 - val_dist_relevant_mae: 2.5194 - val_dist_relevant_mse: 15.9078\n",
      "Epoch 116/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.6049 - prob_loss: 0.0921 - dist_loss: 2.5644 - prob_kld: 0.0138 - dist_relevant_mae: 2.5638 - dist_relevant_mse: 18.7699 - val_loss: 0.5701 - val_prob_loss: 0.0808 - val_dist_loss: 2.4462 - val_prob_kld: 0.0120 - val_dist_relevant_mae: 2.4456 - val_dist_relevant_mse: 15.6718\n",
      "Epoch 117/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5794 - prob_loss: 0.0836 - dist_loss: 2.4788 - prob_kld: 0.0142 - dist_relevant_mae: 2.4782 - dist_relevant_mse: 17.2725 - val_loss: 0.5533 - val_prob_loss: 0.0877 - val_dist_loss: 2.3276 - val_prob_kld: 0.0189 - val_dist_relevant_mae: 2.3271 - val_dist_relevant_mse: 16.0908\n",
      "Epoch 118/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.7205 - prob_loss: 0.1073 - dist_loss: 3.0661 - prob_kld: 0.0206 - dist_relevant_mae: 3.0655 - dist_relevant_mse: 26.4901 - val_loss: 0.5439 - val_prob_loss: 0.0812 - val_dist_loss: 2.3137 - val_prob_kld: 0.0123 - val_dist_relevant_mae: 2.3132 - val_dist_relevant_mse: 15.6973\n",
      "Epoch 119/200\n",
      "10/10 [==============================] - 9s 904ms/step - loss: 0.7287 - prob_loss: 0.0860 - dist_loss: 3.2131 - prob_kld: 0.0144 - dist_relevant_mae: 3.2126 - dist_relevant_mse: 31.1650 - val_loss: 0.5448 - val_prob_loss: 0.0811 - val_dist_loss: 2.3188 - val_prob_kld: 0.0122 - val_dist_relevant_mae: 2.3183 - val_dist_relevant_mse: 16.5224\n",
      "Epoch 120/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.6726 - prob_loss: 0.1171 - dist_loss: 2.7773 - prob_kld: 0.0205 - dist_relevant_mae: 2.7768 - dist_relevant_mse: 22.5494 - val_loss: 0.6492 - val_prob_loss: 0.0837 - val_dist_loss: 2.8277 - val_prob_kld: 0.0149 - val_dist_relevant_mae: 2.8271 - val_dist_relevant_mse: 18.4238\n",
      "Epoch 121/200\n",
      "10/10 [==============================] - 9s 880ms/step - loss: 0.6576 - prob_loss: 0.0995 - dist_loss: 2.7902 - prob_kld: 0.0199 - dist_relevant_mae: 2.7897 - dist_relevant_mse: 20.9133 - val_loss: 0.5614 - val_prob_loss: 0.0816 - val_dist_loss: 2.3991 - val_prob_kld: 0.0127 - val_dist_relevant_mae: 2.3986 - val_dist_relevant_mse: 15.3008\n",
      "Epoch 122/200\n",
      "10/10 [==============================] - 9s 879ms/step - loss: 0.6077 - prob_loss: 0.0822 - dist_loss: 2.6274 - prob_kld: 0.0110 - dist_relevant_mae: 2.6269 - dist_relevant_mse: 21.3762 - val_loss: 0.5811 - val_prob_loss: 0.0807 - val_dist_loss: 2.5023 - val_prob_kld: 0.0118 - val_dist_relevant_mae: 2.5018 - val_dist_relevant_mse: 15.6003\n",
      "Epoch 123/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.6688 - prob_loss: 0.0934 - dist_loss: 2.8769 - prob_kld: 0.0148 - dist_relevant_mae: 2.8763 - dist_relevant_mse: 24.1716 - val_loss: 0.5860 - val_prob_loss: 0.0823 - val_dist_loss: 2.5182 - val_prob_kld: 0.0135 - val_dist_relevant_mae: 2.5177 - val_dist_relevant_mse: 15.8188\n",
      "Epoch 124/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6671 - prob_loss: 0.0856 - dist_loss: 2.9077 - prob_kld: 0.0162 - dist_relevant_mae: 2.9071 - dist_relevant_mse: 22.9727 - val_loss: 0.5511 - val_prob_loss: 0.0842 - val_dist_loss: 2.3347 - val_prob_kld: 0.0154 - val_dist_relevant_mae: 2.3342 - val_dist_relevant_mse: 15.3521\n",
      "Epoch 125/200\n",
      "10/10 [==============================] - 9s 875ms/step - loss: 0.6069 - prob_loss: 0.0865 - dist_loss: 2.6019 - prob_kld: 0.0193 - dist_relevant_mae: 2.6014 - dist_relevant_mse: 20.3374 - val_loss: 0.5681 - val_prob_loss: 0.0809 - val_dist_loss: 2.4357 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.4352 - val_dist_relevant_mse: 16.4046\n",
      "Epoch 126/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.6271 - prob_loss: 0.0810 - dist_loss: 2.7304 - prob_kld: 0.0126 - dist_relevant_mae: 2.7299 - dist_relevant_mse: 22.6959 - val_loss: 0.5393 - val_prob_loss: 0.0810 - val_dist_loss: 2.2915 - val_prob_kld: 0.0122 - val_dist_relevant_mae: 2.2910 - val_dist_relevant_mse: 15.1463\n",
      "Epoch 127/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5834 - prob_loss: 0.0758 - dist_loss: 2.5380 - prob_kld: 0.0125 - dist_relevant_mae: 2.5375 - dist_relevant_mse: 17.7716 - val_loss: 0.5431 - val_prob_loss: 0.0804 - val_dist_loss: 2.3134 - val_prob_kld: 0.0115 - val_dist_relevant_mae: 2.3128 - val_dist_relevant_mse: 15.0656\n",
      "Epoch 128/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.6150 - prob_loss: 0.1025 - dist_loss: 2.5627 - prob_kld: 0.0155 - dist_relevant_mae: 2.5621 - dist_relevant_mse: 19.7146 - val_loss: 0.5414 - val_prob_loss: 0.0802 - val_dist_loss: 2.3063 - val_prob_kld: 0.0113 - val_dist_relevant_mae: 2.3058 - val_dist_relevant_mse: 16.3326\n",
      "Epoch 129/200\n",
      "10/10 [==============================] - 9s 902ms/step - loss: 0.7315 - prob_loss: 0.1194 - dist_loss: 3.0605 - prob_kld: 0.0157 - dist_relevant_mae: 3.0599 - dist_relevant_mse: 28.0609 - val_loss: 0.5322 - val_prob_loss: 0.0807 - val_dist_loss: 2.2576 - val_prob_kld: 0.0119 - val_dist_relevant_mae: 2.2571 - val_dist_relevant_mse: 14.6147\n",
      "Epoch 130/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 0.6110 - prob_loss: 0.0854 - dist_loss: 2.6281 - prob_kld: 0.0117 - dist_relevant_mae: 2.6276 - dist_relevant_mse: 19.4255 - val_loss: 0.5359 - val_prob_loss: 0.0809 - val_dist_loss: 2.2750 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.2745 - val_dist_relevant_mse: 15.3927\n",
      "Epoch 131/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.5971 - prob_loss: 0.0841 - dist_loss: 2.5652 - prob_kld: 0.0153 - dist_relevant_mae: 2.5647 - dist_relevant_mse: 19.1791 - val_loss: 0.5548 - val_prob_loss: 0.0823 - val_dist_loss: 2.3628 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.3623 - val_dist_relevant_mse: 16.2102\n",
      "Epoch 132/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 0.6619 - prob_loss: 0.0988 - dist_loss: 2.8155 - prob_kld: 0.0197 - dist_relevant_mae: 2.8150 - dist_relevant_mse: 22.8242 - val_loss: 0.5675 - val_prob_loss: 0.0810 - val_dist_loss: 2.4324 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.4319 - val_dist_relevant_mse: 17.7878\n",
      "Epoch 133/200\n",
      "10/10 [==============================] - 9s 895ms/step - loss: 0.6055 - prob_loss: 0.0707 - dist_loss: 2.6742 - prob_kld: 0.0121 - dist_relevant_mae: 2.6737 - dist_relevant_mse: 27.7430 - val_loss: 0.5333 - val_prob_loss: 0.0849 - val_dist_loss: 2.2421 - val_prob_kld: 0.0160 - val_dist_relevant_mae: 2.2415 - val_dist_relevant_mse: 15.1945\n",
      "Epoch 134/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.6467 - prob_loss: 0.1053 - dist_loss: 2.7073 - prob_kld: 0.0205 - dist_relevant_mae: 2.7068 - dist_relevant_mse: 20.2223 - val_loss: 0.5219 - val_prob_loss: 0.0818 - val_dist_loss: 2.2005 - val_prob_kld: 0.0129 - val_dist_relevant_mae: 2.2000 - val_dist_relevant_mse: 14.5073\n",
      "Epoch 135/200\n",
      "10/10 [==============================] - 9s 901ms/step - loss: 0.4319 - prob_loss: 0.0626 - dist_loss: 1.8469 - prob_kld: 0.0086 - dist_relevant_mae: 1.8464 - dist_relevant_mse: 11.6336 - val_loss: 0.5212 - val_prob_loss: 0.0809 - val_dist_loss: 2.2015 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.2010 - val_dist_relevant_mse: 14.0051\n",
      "Epoch 136/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.5174 - prob_loss: 0.0780 - dist_loss: 2.1971 - prob_kld: 0.0116 - dist_relevant_mae: 2.1966 - dist_relevant_mse: 15.5983 - val_loss: 0.5191 - val_prob_loss: 0.0800 - val_dist_loss: 2.1955 - val_prob_kld: 0.0111 - val_dist_relevant_mae: 2.1950 - val_dist_relevant_mse: 14.1002\n",
      "Epoch 137/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6021 - prob_loss: 0.1004 - dist_loss: 2.5081 - prob_kld: 0.0148 - dist_relevant_mae: 2.5076 - dist_relevant_mse: 18.0232 - val_loss: 0.5437 - val_prob_loss: 0.0823 - val_dist_loss: 2.3071 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.3065 - val_dist_relevant_mse: 15.9326\n",
      "Epoch 138/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.5540 - prob_loss: 0.0861 - dist_loss: 2.3394 - prob_kld: 0.0146 - dist_relevant_mae: 2.3388 - dist_relevant_mse: 16.0472 - val_loss: 0.5222 - val_prob_loss: 0.0808 - val_dist_loss: 2.2068 - val_prob_kld: 0.0120 - val_dist_relevant_mae: 2.2063 - val_dist_relevant_mse: 14.5601\n",
      "Epoch 139/200\n",
      "10/10 [==============================] - 9s 894ms/step - loss: 0.5944 - prob_loss: 0.0833 - dist_loss: 2.5557 - prob_kld: 0.0139 - dist_relevant_mae: 2.5552 - dist_relevant_mse: 19.1875 - val_loss: 0.5293 - val_prob_loss: 0.0799 - val_dist_loss: 2.2465 - val_prob_kld: 0.0111 - val_dist_relevant_mae: 2.2460 - val_dist_relevant_mse: 13.7433\n",
      "Epoch 140/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.5170 - prob_loss: 0.0798 - dist_loss: 2.1856 - prob_kld: 0.0183 - dist_relevant_mae: 2.1851 - dist_relevant_mse: 16.6601 - val_loss: 0.5359 - val_prob_loss: 0.0818 - val_dist_loss: 2.2703 - val_prob_kld: 0.0130 - val_dist_relevant_mae: 2.2697 - val_dist_relevant_mse: 13.9434\n",
      "Epoch 141/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6368 - prob_loss: 0.0878 - dist_loss: 2.7451 - prob_kld: 0.0139 - dist_relevant_mae: 2.7446 - dist_relevant_mse: 21.6177 - val_loss: 0.5760 - val_prob_loss: 0.0806 - val_dist_loss: 2.4772 - val_prob_kld: 0.0117 - val_dist_relevant_mae: 2.4766 - val_dist_relevant_mse: 15.4397\n",
      "Epoch 142/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.5818 - prob_loss: 0.0868 - dist_loss: 2.4748 - prob_kld: 0.0165 - dist_relevant_mae: 2.4743 - dist_relevant_mse: 16.9816 - val_loss: 0.5167 - val_prob_loss: 0.0796 - val_dist_loss: 2.1855 - val_prob_kld: 0.0108 - val_dist_relevant_mae: 2.1850 - val_dist_relevant_mse: 13.6571\n",
      "Epoch 143/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.6570 - prob_loss: 0.0854 - dist_loss: 2.8581 - prob_kld: 0.0127 - dist_relevant_mae: 2.8575 - dist_relevant_mse: 24.4734 - val_loss: 0.5359 - val_prob_loss: 0.0856 - val_dist_loss: 2.2516 - val_prob_kld: 0.0167 - val_dist_relevant_mae: 2.2511 - val_dist_relevant_mse: 13.5768\n",
      "Epoch 144/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.5971 - prob_loss: 0.0933 - dist_loss: 2.5193 - prob_kld: 0.0202 - dist_relevant_mae: 2.5189 - dist_relevant_mse: 22.8309 - val_loss: 0.5302 - val_prob_loss: 0.0822 - val_dist_loss: 2.2398 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.2393 - val_dist_relevant_mse: 13.9075\n",
      "Epoch 145/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5346 - prob_loss: 0.0820 - dist_loss: 2.2633 - prob_kld: 0.0136 - dist_relevant_mae: 2.2628 - dist_relevant_mse: 15.1390 - val_loss: 0.5158 - val_prob_loss: 0.0797 - val_dist_loss: 2.1804 - val_prob_kld: 0.0109 - val_dist_relevant_mae: 2.1799 - val_dist_relevant_mse: 13.6927\n",
      "Epoch 146/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.6931 - prob_loss: 0.1164 - dist_loss: 2.8834 - prob_kld: 0.0182 - dist_relevant_mae: 2.8828 - dist_relevant_mse: 22.9035 - val_loss: 0.5088 - val_prob_loss: 0.0800 - val_dist_loss: 2.1442 - val_prob_kld: 0.0111 - val_dist_relevant_mae: 2.1438 - val_dist_relevant_mse: 13.7495\n",
      "Epoch 147/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.5603 - prob_loss: 0.0817 - dist_loss: 2.3931 - prob_kld: 0.0129 - dist_relevant_mae: 2.3926 - dist_relevant_mse: 17.7169 - val_loss: 0.5763 - val_prob_loss: 0.0805 - val_dist_loss: 2.4793 - val_prob_kld: 0.0117 - val_dist_relevant_mae: 2.4788 - val_dist_relevant_mse: 18.4887\n",
      "Epoch 148/200\n",
      "10/10 [==============================] - 9s 896ms/step - loss: 0.6330 - prob_loss: 0.0934 - dist_loss: 2.6982 - prob_kld: 0.0156 - dist_relevant_mae: 2.6977 - dist_relevant_mse: 19.3422 - val_loss: 0.5513 - val_prob_loss: 0.0823 - val_dist_loss: 2.3450 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.3446 - val_dist_relevant_mse: 15.7677\n",
      "Epoch 149/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.6331 - prob_loss: 0.0862 - dist_loss: 2.7344 - prob_kld: 0.0220 - dist_relevant_mae: 2.7339 - dist_relevant_mse: 19.5987 - val_loss: 0.5330 - val_prob_loss: 0.0821 - val_dist_loss: 2.2542 - val_prob_kld: 0.0133 - val_dist_relevant_mae: 2.2537 - val_dist_relevant_mse: 15.3705\n",
      "Epoch 150/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 0.6311 - prob_loss: 0.0937 - dist_loss: 2.6866 - prob_kld: 0.0180 - dist_relevant_mae: 2.6861 - dist_relevant_mse: 20.6559 - val_loss: 0.5264 - val_prob_loss: 0.0804 - val_dist_loss: 2.2300 - val_prob_kld: 0.0116 - val_dist_relevant_mae: 2.2294 - val_dist_relevant_mse: 14.2436\n",
      "Epoch 151/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.7124 - prob_loss: 0.1039 - dist_loss: 3.0427 - prob_kld: 0.0198 - dist_relevant_mae: 3.0421 - dist_relevant_mse: 27.2239 - val_loss: 0.5367 - val_prob_loss: 0.0822 - val_dist_loss: 2.2726 - val_prob_kld: 0.0134 - val_dist_relevant_mae: 2.2721 - val_dist_relevant_mse: 13.6512\n",
      "Epoch 152/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.4511 - prob_loss: 0.0722 - dist_loss: 1.8946 - prob_kld: 0.0110 - dist_relevant_mae: 1.8942 - dist_relevant_mse: 10.9429 - val_loss: 0.5396 - val_prob_loss: 0.0798 - val_dist_loss: 2.2988 - val_prob_kld: 0.0110 - val_dist_relevant_mae: 2.2984 - val_dist_relevant_mse: 16.4798\n",
      "Epoch 153/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.5042 - prob_loss: 0.0813 - dist_loss: 2.1144 - prob_kld: 0.0107 - dist_relevant_mae: 2.1139 - dist_relevant_mse: 15.0342 - val_loss: 0.5376 - val_prob_loss: 0.0796 - val_dist_loss: 2.2899 - val_prob_kld: 0.0107 - val_dist_relevant_mae: 2.2894 - val_dist_relevant_mse: 13.6193\n",
      "Epoch 154/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.7879 - prob_loss: 0.1024 - dist_loss: 3.4277 - prob_kld: 0.0174 - dist_relevant_mae: 3.4271 - dist_relevant_mse: 34.6463 - val_loss: 0.5144 - val_prob_loss: 0.0809 - val_dist_loss: 2.1673 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.1667 - val_dist_relevant_mse: 14.1526\n",
      "Epoch 155/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.6248 - prob_loss: 0.0811 - dist_loss: 2.7187 - prob_kld: 0.0145 - dist_relevant_mae: 2.7182 - dist_relevant_mse: 20.6338 - val_loss: 0.6169 - val_prob_loss: 0.0817 - val_dist_loss: 2.6759 - val_prob_kld: 0.0129 - val_dist_relevant_mae: 2.6755 - val_dist_relevant_mse: 19.5972\n",
      "Epoch 156/200\n",
      "10/10 [==============================] - 9s 879ms/step - loss: 0.7609 - prob_loss: 0.1026 - dist_loss: 3.2915 - prob_kld: 0.0148 - dist_relevant_mae: 3.2910 - dist_relevant_mse: 30.4630 - val_loss: 0.5390 - val_prob_loss: 0.0806 - val_dist_loss: 2.2918 - val_prob_kld: 0.0117 - val_dist_relevant_mae: 2.2914 - val_dist_relevant_mse: 16.1446\n",
      "Epoch 157/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.6618 - prob_loss: 0.0789 - dist_loss: 2.9145 - prob_kld: 0.0134 - dist_relevant_mae: 2.9140 - dist_relevant_mse: 24.6808 - val_loss: 0.6145 - val_prob_loss: 0.0808 - val_dist_loss: 2.6683 - val_prob_kld: 0.0120 - val_dist_relevant_mae: 2.6678 - val_dist_relevant_mse: 20.8903\n",
      "Epoch 158/200\n",
      "10/10 [==============================] - 9s 881ms/step - loss: 0.6402 - prob_loss: 0.0801 - dist_loss: 2.8007 - prob_kld: 0.0152 - dist_relevant_mae: 2.8002 - dist_relevant_mse: 22.4859 - val_loss: 0.6495 - val_prob_loss: 0.0790 - val_dist_loss: 2.8525 - val_prob_kld: 0.0102 - val_dist_relevant_mae: 2.8521 - val_dist_relevant_mse: 20.3210\n",
      "Epoch 159/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.7229 - prob_loss: 0.0845 - dist_loss: 3.1918 - prob_kld: 0.0113 - dist_relevant_mae: 3.1913 - dist_relevant_mse: 27.7272 - val_loss: 0.5560 - val_prob_loss: 0.0802 - val_dist_loss: 2.3788 - val_prob_kld: 0.0114 - val_dist_relevant_mae: 2.3784 - val_dist_relevant_mse: 17.3086\n",
      "Epoch 160/200\n",
      "10/10 [==============================] - 9s 901ms/step - loss: 0.6336 - prob_loss: 0.0978 - dist_loss: 2.6787 - prob_kld: 0.0129 - dist_relevant_mae: 2.6781 - dist_relevant_mse: 20.5590 - val_loss: 0.4979 - val_prob_loss: 0.0794 - val_dist_loss: 2.0923 - val_prob_kld: 0.0106 - val_dist_relevant_mae: 2.0918 - val_dist_relevant_mse: 12.8079\n",
      "Epoch 161/200\n",
      "10/10 [==============================] - 9s 895ms/step - loss: 0.6327 - prob_loss: 0.1005 - dist_loss: 2.6612 - prob_kld: 0.0150 - dist_relevant_mae: 2.6607 - dist_relevant_mse: 20.2682 - val_loss: 0.5284 - val_prob_loss: 0.0807 - val_dist_loss: 2.2388 - val_prob_kld: 0.0118 - val_dist_relevant_mae: 2.2383 - val_dist_relevant_mse: 15.3200\n",
      "Epoch 162/200\n",
      "10/10 [==============================] - 9s 877ms/step - loss: 0.5995 - prob_loss: 0.0855 - dist_loss: 2.5700 - prob_kld: 0.0115 - dist_relevant_mae: 2.5695 - dist_relevant_mse: 18.3839 - val_loss: 0.5346 - val_prob_loss: 0.0795 - val_dist_loss: 2.2756 - val_prob_kld: 0.0107 - val_dist_relevant_mae: 2.2751 - val_dist_relevant_mse: 15.6994\n",
      "Epoch 163/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.6306 - prob_loss: 0.0848 - dist_loss: 2.7289 - prob_kld: 0.0111 - dist_relevant_mae: 2.7284 - dist_relevant_mse: 21.6130 - val_loss: 0.4946 - val_prob_loss: 0.0798 - val_dist_loss: 2.0741 - val_prob_kld: 0.0109 - val_dist_relevant_mae: 2.0736 - val_dist_relevant_mse: 12.5891\n",
      "Epoch 164/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.4989 - prob_loss: 0.0748 - dist_loss: 2.1204 - prob_kld: 0.0127 - dist_relevant_mae: 2.1200 - dist_relevant_mse: 15.0885 - val_loss: 0.5113 - val_prob_loss: 0.0792 - val_dist_loss: 2.1606 - val_prob_kld: 0.0104 - val_dist_relevant_mae: 2.1601 - val_dist_relevant_mse: 12.6935\n",
      "Epoch 165/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.6108 - prob_loss: 0.0959 - dist_loss: 2.5744 - prob_kld: 0.0131 - dist_relevant_mae: 2.5739 - dist_relevant_mse: 19.1483 - val_loss: 0.5210 - val_prob_loss: 0.0846 - val_dist_loss: 2.1820 - val_prob_kld: 0.0158 - val_dist_relevant_mae: 2.1814 - val_dist_relevant_mse: 12.6666\n",
      "Epoch 166/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.4209 - prob_loss: 0.0663 - dist_loss: 1.7732 - prob_kld: 0.0162 - dist_relevant_mae: 1.7727 - dist_relevant_mse: 9.4026 - val_loss: 0.5229 - val_prob_loss: 0.0818 - val_dist_loss: 2.2054 - val_prob_kld: 0.0129 - val_dist_relevant_mae: 2.2048 - val_dist_relevant_mse: 13.4337\n",
      "Epoch 167/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 0.6735 - prob_loss: 0.1037 - dist_loss: 2.8493 - prob_kld: 0.0197 - dist_relevant_mae: 2.8487 - dist_relevant_mse: 23.1313 - val_loss: 0.5800 - val_prob_loss: 0.0829 - val_dist_loss: 2.4857 - val_prob_kld: 0.0140 - val_dist_relevant_mae: 2.4852 - val_dist_relevant_mse: 19.0388\n",
      "Epoch 168/200\n",
      "10/10 [==============================] - 9s 886ms/step - loss: 0.5733 - prob_loss: 0.0902 - dist_loss: 2.4159 - prob_kld: 0.0164 - dist_relevant_mae: 2.4154 - dist_relevant_mse: 17.9476 - val_loss: 0.4977 - val_prob_loss: 0.0817 - val_dist_loss: 2.0799 - val_prob_kld: 0.0129 - val_dist_relevant_mae: 2.0794 - val_dist_relevant_mse: 13.1007\n",
      "Epoch 169/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.5808 - prob_loss: 0.0921 - dist_loss: 2.4436 - prob_kld: 0.0158 - dist_relevant_mae: 2.4430 - dist_relevant_mse: 15.6900 - val_loss: 0.5098 - val_prob_loss: 0.0809 - val_dist_loss: 2.1442 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 2.1437 - val_dist_relevant_mse: 14.2836\n",
      "Epoch 170/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5597 - prob_loss: 0.0840 - dist_loss: 2.3783 - prob_kld: 0.0135 - dist_relevant_mae: 2.3777 - dist_relevant_mse: 15.8874 - val_loss: 0.5214 - val_prob_loss: 0.0824 - val_dist_loss: 2.1950 - val_prob_kld: 0.0135 - val_dist_relevant_mae: 2.1944 - val_dist_relevant_mse: 13.0302\n",
      "Epoch 171/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.5508 - prob_loss: 0.0875 - dist_loss: 2.3167 - prob_kld: 0.0124 - dist_relevant_mae: 2.3161 - dist_relevant_mse: 15.9318 - val_loss: 0.5514 - val_prob_loss: 0.0799 - val_dist_loss: 2.3572 - val_prob_kld: 0.0111 - val_dist_relevant_mae: 2.3567 - val_dist_relevant_mse: 13.9169\n",
      "Epoch 172/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6250 - prob_loss: 0.0792 - dist_loss: 2.7294 - prob_kld: 0.0130 - dist_relevant_mae: 2.7288 - dist_relevant_mse: 22.0020 - val_loss: 0.5157 - val_prob_loss: 0.0807 - val_dist_loss: 2.1750 - val_prob_kld: 0.0119 - val_dist_relevant_mae: 2.1745 - val_dist_relevant_mse: 12.4134\n",
      "Epoch 173/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.5537 - prob_loss: 0.0838 - dist_loss: 2.3496 - prob_kld: 0.0156 - dist_relevant_mae: 2.3491 - dist_relevant_mse: 16.1300 - val_loss: 0.5563 - val_prob_loss: 0.0796 - val_dist_loss: 2.3835 - val_prob_kld: 0.0108 - val_dist_relevant_mae: 2.3829 - val_dist_relevant_mse: 13.4719\n",
      "Epoch 174/200\n",
      "10/10 [==============================] - 9s 897ms/step - loss: 0.6421 - prob_loss: 0.0924 - dist_loss: 2.7487 - prob_kld: 0.0148 - dist_relevant_mae: 2.7482 - dist_relevant_mse: 21.1913 - val_loss: 0.5072 - val_prob_loss: 0.0813 - val_dist_loss: 2.1294 - val_prob_kld: 0.0125 - val_dist_relevant_mae: 2.1289 - val_dist_relevant_mse: 12.2187\n",
      "Epoch 175/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6090 - prob_loss: 0.0901 - dist_loss: 2.5948 - prob_kld: 0.0175 - dist_relevant_mae: 2.5943 - dist_relevant_mse: 20.3911 - val_loss: 0.4941 - val_prob_loss: 0.0796 - val_dist_loss: 2.0726 - val_prob_kld: 0.0107 - val_dist_relevant_mae: 2.0721 - val_dist_relevant_mse: 12.3641\n",
      "Epoch 176/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 0.5501 - prob_loss: 0.0813 - dist_loss: 2.3438 - prob_kld: 0.0116 - dist_relevant_mae: 2.3433 - dist_relevant_mse: 16.3928 - val_loss: 0.5158 - val_prob_loss: 0.0799 - val_dist_loss: 2.1795 - val_prob_kld: 0.0110 - val_dist_relevant_mae: 2.1789 - val_dist_relevant_mse: 12.5307\n",
      "Epoch 177/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.4811 - prob_loss: 0.0732 - dist_loss: 2.0395 - prob_kld: 0.0098 - dist_relevant_mae: 2.0390 - dist_relevant_mse: 11.9226 - val_loss: 0.4859 - val_prob_loss: 0.0798 - val_dist_loss: 2.0302 - val_prob_kld: 0.0110 - val_dist_relevant_mae: 2.0297 - val_dist_relevant_mse: 12.0296\n",
      "Epoch 178/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5036 - prob_loss: 0.0906 - dist_loss: 2.0649 - prob_kld: 0.0158 - dist_relevant_mae: 2.0644 - dist_relevant_mse: 14.3341 - val_loss: 0.4816 - val_prob_loss: 0.0793 - val_dist_loss: 2.0116 - val_prob_kld: 0.0104 - val_dist_relevant_mae: 2.0111 - val_dist_relevant_mse: 11.8216\n",
      "Epoch 179/200\n",
      "10/10 [==============================] - 9s 882ms/step - loss: 0.4851 - prob_loss: 0.0925 - dist_loss: 1.9631 - prob_kld: 0.0145 - dist_relevant_mae: 1.9626 - dist_relevant_mse: 11.1727 - val_loss: 0.4793 - val_prob_loss: 0.0792 - val_dist_loss: 2.0005 - val_prob_kld: 0.0103 - val_dist_relevant_mae: 2.0001 - val_dist_relevant_mse: 12.1647\n",
      "Epoch 180/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 0.5957 - prob_loss: 0.1037 - dist_loss: 2.4604 - prob_kld: 0.0146 - dist_relevant_mae: 2.4599 - dist_relevant_mse: 17.8518 - val_loss: 0.4911 - val_prob_loss: 0.0795 - val_dist_loss: 2.0583 - val_prob_kld: 0.0106 - val_dist_relevant_mae: 2.0578 - val_dist_relevant_mse: 12.6378\n",
      "Epoch 181/200\n",
      "10/10 [==============================] - 9s 890ms/step - loss: 0.6009 - prob_loss: 0.1063 - dist_loss: 2.4733 - prob_kld: 0.0150 - dist_relevant_mae: 2.4728 - dist_relevant_mse: 16.5767 - val_loss: 0.5094 - val_prob_loss: 0.0793 - val_dist_loss: 2.1503 - val_prob_kld: 0.0105 - val_dist_relevant_mae: 2.1498 - val_dist_relevant_mse: 12.8429\n",
      "Epoch 182/200\n",
      "10/10 [==============================] - 9s 878ms/step - loss: 0.5670 - prob_loss: 0.0936 - dist_loss: 2.3667 - prob_kld: 0.0159 - dist_relevant_mae: 2.3662 - dist_relevant_mse: 16.0929 - val_loss: 0.4936 - val_prob_loss: 0.0793 - val_dist_loss: 2.0718 - val_prob_kld: 0.0104 - val_dist_relevant_mae: 2.0713 - val_dist_relevant_mse: 12.0921\n",
      "Epoch 183/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5852 - prob_loss: 0.0912 - dist_loss: 2.4697 - prob_kld: 0.0117 - dist_relevant_mae: 2.4692 - dist_relevant_mse: 17.8890 - val_loss: 0.4941 - val_prob_loss: 0.0791 - val_dist_loss: 2.0751 - val_prob_kld: 0.0103 - val_dist_relevant_mae: 2.0746 - val_dist_relevant_mse: 12.5728\n",
      "Epoch 184/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.5627 - prob_loss: 0.0917 - dist_loss: 2.3552 - prob_kld: 0.0124 - dist_relevant_mae: 2.3547 - dist_relevant_mse: 16.7868 - val_loss: 0.5322 - val_prob_loss: 0.0798 - val_dist_loss: 2.2621 - val_prob_kld: 0.0109 - val_dist_relevant_mae: 2.2617 - val_dist_relevant_mse: 15.3926\n",
      "Epoch 185/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.6161 - prob_loss: 0.0883 - dist_loss: 2.6387 - prob_kld: 0.0138 - dist_relevant_mae: 2.6383 - dist_relevant_mse: 22.2553 - val_loss: 0.4902 - val_prob_loss: 0.0792 - val_dist_loss: 2.0549 - val_prob_kld: 0.0104 - val_dist_relevant_mae: 2.0545 - val_dist_relevant_mse: 12.6454\n",
      "Epoch 186/200\n",
      "10/10 [==============================] - 9s 888ms/step - loss: 0.4927 - prob_loss: 0.0642 - dist_loss: 2.1425 - prob_kld: 0.0106 - dist_relevant_mae: 2.1420 - dist_relevant_mse: 14.9577 - val_loss: 0.5130 - val_prob_loss: 0.0795 - val_dist_loss: 2.1676 - val_prob_kld: 0.0107 - val_dist_relevant_mae: 2.1671 - val_dist_relevant_mse: 13.9810\n",
      "Epoch 187/200\n",
      "10/10 [==============================] - 9s 897ms/step - loss: 0.6347 - prob_loss: 0.1024 - dist_loss: 2.6613 - prob_kld: 0.0143 - dist_relevant_mae: 2.6607 - dist_relevant_mse: 20.3218 - val_loss: 0.4806 - val_prob_loss: 0.0799 - val_dist_loss: 2.0036 - val_prob_kld: 0.0110 - val_dist_relevant_mae: 2.0031 - val_dist_relevant_mse: 12.2568\n",
      "Epoch 188/200\n",
      "10/10 [==============================] - 9s 887ms/step - loss: 0.5936 - prob_loss: 0.0844 - dist_loss: 2.5460 - prob_kld: 0.0137 - dist_relevant_mae: 2.5455 - dist_relevant_mse: 21.0332 - val_loss: 0.4911 - val_prob_loss: 0.0820 - val_dist_loss: 2.0457 - val_prob_kld: 0.0131 - val_dist_relevant_mae: 2.0451 - val_dist_relevant_mse: 12.3039\n",
      "Epoch 189/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.5892 - prob_loss: 0.1168 - dist_loss: 2.3620 - prob_kld: 0.0233 - dist_relevant_mae: 2.3613 - dist_relevant_mse: 16.7672 - val_loss: 0.4873 - val_prob_loss: 0.0808 - val_dist_loss: 2.0325 - val_prob_kld: 0.0120 - val_dist_relevant_mae: 2.0320 - val_dist_relevant_mse: 12.3037\n",
      "Epoch 190/200\n",
      "10/10 [==============================] - 9s 892ms/step - loss: 0.5258 - prob_loss: 0.0766 - dist_loss: 2.2456 - prob_kld: 0.0104 - dist_relevant_mae: 2.2451 - dist_relevant_mse: 16.4049 - val_loss: 0.5069 - val_prob_loss: 0.0817 - val_dist_loss: 2.1260 - val_prob_kld: 0.0128 - val_dist_relevant_mae: 2.1255 - val_dist_relevant_mse: 13.2126\n",
      "Epoch 191/200\n",
      "10/10 [==============================] - 9s 893ms/step - loss: 0.5516 - prob_loss: 0.0911 - dist_loss: 2.3022 - prob_kld: 0.0180 - dist_relevant_mae: 2.3017 - dist_relevant_mse: 15.0308 - val_loss: 0.4836 - val_prob_loss: 0.0791 - val_dist_loss: 2.0226 - val_prob_kld: 0.0103 - val_dist_relevant_mae: 2.0222 - val_dist_relevant_mse: 11.8228\n",
      "Epoch 192/200\n",
      "10/10 [==============================] - 9s 885ms/step - loss: 0.5986 - prob_loss: 0.0887 - dist_loss: 2.5496 - prob_kld: 0.0121 - dist_relevant_mae: 2.5491 - dist_relevant_mse: 21.2026 - val_loss: 0.4770 - val_prob_loss: 0.0791 - val_dist_loss: 1.9895 - val_prob_kld: 0.0103 - val_dist_relevant_mae: 1.9890 - val_dist_relevant_mse: 11.7019\n",
      "Epoch 193/200\n",
      "10/10 [==============================] - 9s 884ms/step - loss: 0.5178 - prob_loss: 0.0793 - dist_loss: 2.1928 - prob_kld: 0.0100 - dist_relevant_mae: 2.1922 - dist_relevant_mse: 14.9629 - val_loss: 0.5127 - val_prob_loss: 0.0803 - val_dist_loss: 2.1618 - val_prob_kld: 0.0115 - val_dist_relevant_mae: 2.1613 - val_dist_relevant_mse: 12.3405\n",
      "Epoch 194/200\n",
      "10/10 [==============================] - 9s 880ms/step - loss: 0.4438 - prob_loss: 0.0582 - dist_loss: 1.9279 - prob_kld: 0.0085 - dist_relevant_mae: 1.9274 - dist_relevant_mse: 10.8783 - val_loss: 0.4780 - val_prob_loss: 0.0788 - val_dist_loss: 1.9959 - val_prob_kld: 0.0100 - val_dist_relevant_mae: 1.9954 - val_dist_relevant_mse: 12.0442\n",
      "Epoch 195/200\n",
      "10/10 [==============================] - 9s 896ms/step - loss: 0.5350 - prob_loss: 0.0872 - dist_loss: 2.2387 - prob_kld: 0.0119 - dist_relevant_mae: 2.2381 - dist_relevant_mse: 13.7933 - val_loss: 0.4981 - val_prob_loss: 0.0787 - val_dist_loss: 2.0969 - val_prob_kld: 0.0098 - val_dist_relevant_mae: 2.0964 - val_dist_relevant_mse: 11.5418\n",
      "Epoch 196/200\n",
      "10/10 [==============================] - 9s 889ms/step - loss: 0.6374 - prob_loss: 0.0953 - dist_loss: 2.7104 - prob_kld: 0.0106 - dist_relevant_mae: 2.7098 - dist_relevant_mse: 21.4278 - val_loss: 0.4695 - val_prob_loss: 0.0791 - val_dist_loss: 1.9521 - val_prob_kld: 0.0102 - val_dist_relevant_mae: 1.9516 - val_dist_relevant_mse: 11.2052\n",
      "Epoch 197/200\n",
      "10/10 [==============================] - 9s 871ms/step - loss: 0.5617 - prob_loss: 0.0786 - dist_loss: 2.4153 - prob_kld: 0.0115 - dist_relevant_mae: 2.4149 - dist_relevant_mse: 17.8050 - val_loss: 0.4763 - val_prob_loss: 0.0792 - val_dist_loss: 1.9856 - val_prob_kld: 0.0103 - val_dist_relevant_mae: 1.9851 - val_dist_relevant_mse: 11.9281\n",
      "Epoch 198/200\n",
      "10/10 [==============================] - 9s 891ms/step - loss: 0.5202 - prob_loss: 0.0757 - dist_loss: 2.2224 - prob_kld: 0.0102 - dist_relevant_mae: 2.2219 - dist_relevant_mse: 13.4389 - val_loss: 0.4832 - val_prob_loss: 0.0787 - val_dist_loss: 2.0224 - val_prob_kld: 0.0098 - val_dist_relevant_mae: 2.0219 - val_dist_relevant_mse: 11.4958\n",
      "Epoch 199/200\n",
      "10/10 [==============================] - 9s 883ms/step - loss: 0.6931 - prob_loss: 0.1086 - dist_loss: 2.9226 - prob_kld: 0.0181 - dist_relevant_mae: 2.9221 - dist_relevant_mse: 21.5565 - val_loss: 0.5142 - val_prob_loss: 0.0833 - val_dist_loss: 2.1542 - val_prob_kld: 0.0145 - val_dist_relevant_mae: 2.1537 - val_dist_relevant_mse: 13.3248\n",
      "Epoch 200/200\n",
      "10/10 [==============================] - 8s 867ms/step - loss: 0.5203 - prob_loss: 0.0689 - dist_loss: 2.2569 - prob_kld: 0.0121 - dist_relevant_mae: 2.2564 - dist_relevant_mse: 14.1415 - val_loss: 0.5912 - val_prob_loss: 0.0872 - val_dist_loss: 2.5197 - val_prob_kld: 0.0184 - val_dist_relevant_mae: 2.5192 - val_dist_relevant_mse: 15.7764\n",
      "\n",
      "Loading network weights from 'weights_best.h5'.\n"
     ]
    }
   ],
   "source": [
    "ind = np.random.permutation(len(X))\n",
    "n_val = max(1, int(round(0.15 * len(X))))\n",
    "ind_train, ind_val = ind[:-n_val], ind[-n_val:]\n",
    "\n",
    "X_val, Y_val = [X[i] for i in ind_val]  , [Y[i] for i in ind_val]\n",
    "X_trn, Y_trn = [X[i] for i in ind_train], [Y[i] for i in ind_train] \n",
    "print('number of images: %3d' % len(X))\n",
    "print('- training:       %3d' % len(X_trn))\n",
    "print('- validation:     %3d' % len(X_val))\n",
    "\n",
    "n_epochs = 200\n",
    "steps = 10\n",
    "\n",
    "history = model.train(X_trn, Y_trn, validation_data=(X_val,Y_val), epochs=n_epochs, steps_per_epoch=steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6- Evaluation and testing of the network\n",
    "\n",
    "After the training, we can check the network was able to learn on the data by plotting the loss for the training and validation data. The fact that the loss is decreasing during the training is an indication that the network was able to learn how to segment nuclei. <br>\n",
    "After 200 epochs, the two curves (validation and training) are reaching a plateau, indicating the network is fully trained and is not able to learn more information from the data. Interestingly, the fact that both curves are overlapping until the end is an indication there was no overfitting (https://en.wikipedia.org/wiki/Overfitting). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 494,
       "width": 497
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "plt.rcParams['figure.figsize'] = (10,10) # Make the figures a bit bigger\n",
    "\n",
    "# Plot the evolution of the loss during the training\n",
    "\n",
    "epochs = range(1, n_epochs+1)\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(epochs, loss_values, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss_values, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test whether the network is able to segment nuclei on completely new images, we are using the testing set. Below is an example where an image is segmented by the newly trained model and compared to the original groud-truth model. We can see that the model is able to segment most of the nuclei, despite the wide variety of size and shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 435,
       "width": 907
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = min(16, len(X_test)-1)\n",
    "img = normalize(X_test[i], 1,99.8, axis=(0,1))\n",
    "label_predict, details = model.predict_instances(img)\n",
    "label_GT = fill_label_holes(Y_test[i])\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "plt.subplot(121) \n",
    "plt.imshow(img if img.ndim==2 else img[...,0], clim=(0,1), cmap='gray')\n",
    "plt.imshow(label_predict, cmap=lbl_cmap, alpha=0.5)\n",
    "plt.title('Raw image + prediction')\n",
    "plt.axis('off');\n",
    "\n",
    "plt.subplot(122) \n",
    "plt.imshow(img if img.ndim==2 else img[...,0], clim=(0,1), cmap='gray')\n",
    "plt.imshow(label_GT, cmap=lbl_cmap, alpha=0.5)\n",
    "plt.title('Raw image + ground truth')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7- More resources\n",
    "\n",
    "- the original publication : https://arxiv.org/pdf/1806.03535.pdf\n",
    "- Standford lectures on AI and deep learning : https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv. Lectures 1,5 & 11 are of particular interest for image segmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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