model_eva-v1-dz100_rep1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "from tifffile import imread\n",
    "from csbdeep.utils import Path, normalize\n",
    "\n",
    "from stardist import fill_label_holes, random_label_cmap, calculate_extents, gputools_available\n",
    "from stardist import Rays_GoldenSpiral\n",
    "from stardist.models import Config3D, StarDist3D, StarDistData3D\n",
    "\n",
    "from skimage.segmentation import relabel_sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def allocateOnEmptyGPU():\n",
    "    import os\n",
    "    import re\n",
    "    import numpy as np\n",
    "    from subprocess import check_output\n",
    "\n",
    "    nvidia_smi_output = check_output(['nvidia-smi']).decode(\"utf-8\")\n",
    "    memory_matches = re.findall('\\d+MiB\\s*/\\s*\\d+MiB', nvidia_smi_output)\n",
    "    memory_string = [match.split('MiB')[0] for match in memory_matches]\n",
    "    gpu_memory_usage = list(map(int, memory_string))\n",
    "\n",
    "    os.environ['CUDA_VISIBLE_DEVICES'] = str(np.argmin(gpu_memory_usage))\n",
    "    print('Run on GPU with ID: {}'.format(os.environ['CUDA_VISIBLE_DEVICES']))\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run on GPU with ID: 0\n"
     ]
    }
   ],
   "source": [
    "allocateOnEmptyGPU()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im0.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im1.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im2.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im3.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im4.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im5.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images\\im6.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\valid\\images\\im0.tif\n"
     ]
    }
   ],
   "source": [
    "X_trn_paths = sorted(Path(r'Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\images').glob('*.tif'))\n",
    "X_vld_paths = sorted(Path(r'Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\valid\\images').glob('*.tif'))\n",
    "\n",
    "for p in X_trn_paths:\n",
    "    print(p)\n",
    "    \n",
    "for p in X_vld_paths:\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:04<00:00,  1.51it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.69it/s]\n"
     ]
    }
   ],
   "source": [
    "X_trn = [imread(p) for p in tqdm(X_trn_paths)]\n",
    "X_vld = [imread(p) for p in tqdm(X_vld_paths)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_trn_paths = sorted(Path(r'Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks').glob('*.tif'))\n",
    "Y_vld_paths = sorted(Path(r'Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\valid\\masks').glob('*.tif'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im0.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im1.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im2.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im3.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im4.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im5.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\train\\masks\\im6.tif\n",
      "Y:\\Eric\\2021_Iterative_Biofilm_Annotation\\datasets\\eva-v1-dz100\\valid\\masks\\im0.tif\n"
     ]
    }
   ],
   "source": [
    "for p in Y_trn_paths:\n",
    "    print(p)\n",
    "    \n",
    "for p in Y_vld_paths:\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 10.47it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 12.19it/s]\n"
     ]
    }
   ],
   "source": [
    "Y_trn = [imread(p) for p in tqdm(Y_trn_paths)]\n",
    "Y_vld = [imread(p) for p in tqdm(Y_vld_paths)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trn[2] = X_trn[2][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(201, 719, 297) (201, 719, 297)\n",
      "(90, 421, 491) (90, 421, 491)\n",
      "(75, 197, 237) (75, 197, 237)\n",
      "(119, 223, 243) (119, 223, 243)\n",
      "(72, 647, 541) (72, 647, 541)\n",
      "(234, 487, 592) (234, 487, 592)\n",
      "(83, 402, 422) (83, 402, 422)\n"
     ]
    }
   ],
   "source": [
    "for y, x in zip(Y_trn, X_trn):\n",
    "    print(y.shape, x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_trn, Y_trn, X_vld, Y_vld = tuple([x[:64, :128, :128] for x in X] for X in [X_trn, Y_trn, X_vld, Y_vld])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for y, x in zip(Y_trn, X_trn):\n",
    "#    print(y.shape, x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelname = 'eva-v1_dz100_rep1'\n",
    "basedir = 'models'\n",
    "n_rays = 192\n",
    "del_empty_patches = False\n",
    "percentage = 100\n",
    "train_patch_size = (64, 192, 192)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:02<00:00,  3.09it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  3.26it/s]\n"
     ]
    }
   ],
   "source": [
    "    axis_norm = (0, 1, 2)\n",
    "    X_trn= [normalize(x, 1, 99.8, axis=axis_norm) for x in tqdm(X_trn)]\n",
    "    X_vld= [normalize(x, 1, 99.8, axis=axis_norm) for x in tqdm(X_vld)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "    n_channel = 1\n",
    "\n",
    "    extents = calculate_extents(Y_trn[2])\n",
    "    anisotropy = tuple(np.max(extents) / extents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5833333333333333, 1.0, 1.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anisotropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_gpu = gputools_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_fliprot(img, mask, axis=None): \n",
    "    if axis is None:\n",
    "        axis = tuple(range(mask.ndim))\n",
    "    axis = tuple(axis)\n",
    "            \n",
    "    assert img.ndim>=mask.ndim\n",
    "    perm = tuple(np.random.permutation(axis))\n",
    "    transpose_axis = np.arange(mask.ndim)\n",
    "    for a, p in zip(axis, perm):\n",
    "        transpose_axis[a] = p\n",
    "    transpose_axis = tuple(transpose_axis)\n",
    "    img = img.transpose(transpose_axis + tuple(range(mask.ndim, img.ndim))) \n",
    "    mask = mask.transpose(transpose_axis) \n",
    "    for ax in axis: \n",
    "        if np.random.rand() > 0.5:\n",
    "            img = np.flip(img, axis=ax)\n",
    "            mask = np.flip(mask, axis=ax)\n",
    "    return img, mask \n",
    "\n",
    "def random_intensity_change(img):\n",
    "    img = img*np.random.uniform(0.6,2) + np.random.uniform(-0.2,0.2)\n",
    "    return img\n",
    "\n",
    "def augmenter(x, y):\n",
    "    \"\"\"Augmentation of a single input/label image pair.\n",
    "    x is an input image\n",
    "    y is the corresponding ground-truth label image\n",
    "    \"\"\"\n",
    "    # Note that we only use fliprots along axis=(1,2), i.e. the yx axis \n",
    "    # as 3D microscopy acquisitions are usually not axially symmetric\n",
    "    x, y = random_fliprot(x, y, axis=(1,2))\n",
    "    x = random_intensity_change(x)\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_dim': 3,\n",
       " 'axes': 'ZYXC',\n",
       " 'n_channel_in': 1,\n",
       " 'n_channel_out': 193,\n",
       " 'train_checkpoint': 'weights_best.h5',\n",
       " 'train_checkpoint_last': 'weights_last.h5',\n",
       " 'train_checkpoint_epoch': 'weights_now.h5',\n",
       " 'n_rays': 192,\n",
       " 'grid': (1, 2, 2),\n",
       " 'anisotropy': (1.5833333333333333, 1.0, 1.0),\n",
       " 'backbone': 'resnet',\n",
       " 'rays_json': {'name': 'Rays_GoldenSpiral',\n",
       "  'kwargs': {'n': 192, 'anisotropy': (1.5833333333333333, 1.0, 1.0)}},\n",
       " 'resnet_n_blocks': 4,\n",
       " 'resnet_kernel_size': (3, 3, 3),\n",
       " 'resnet_kernel_init': 'he_normal',\n",
       " 'resnet_n_filter_base': 32,\n",
       " 'resnet_n_conv_per_block': 3,\n",
       " 'resnet_activation': 'relu',\n",
       " 'resnet_batch_norm': False,\n",
       " 'net_conv_after_resnet': 128,\n",
       " 'net_input_shape': (None, None, None, 1),\n",
       " 'net_mask_shape': (None, None, None, 1),\n",
       " 'train_patch_size': (64, 192, 192),\n",
       " 'train_background_reg': 0.0001,\n",
       " 'train_foreground_only': 0.9,\n",
       " 'train_dist_loss': 'mae',\n",
       " 'train_loss_weights': (1, 0.2),\n",
       " 'train_epochs': 400,\n",
       " 'train_steps_per_epoch': 100,\n",
       " 'train_learning_rate': 0.0003,\n",
       " 'train_batch_size': 1,\n",
       " 'train_n_val_patches': None,\n",
       " 'train_tensorboard': True,\n",
       " 'train_reduce_lr': {'factor': 0.5, 'patience': 40, 'min_delta': 0},\n",
       " 'use_gpu': True}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    # Predict on subsampled grid for increased efficiency and larger field of view\n",
    "    grid = tuple(1 if a > 1.5 else 2 for a in anisotropy)\n",
    "\n",
    "    # Use rays on a Fibonacci lattice adjusted for measured anisotropy of the training data\n",
    "    rays = Rays_GoldenSpiral(n_rays, anisotropy=anisotropy)\n",
    "\n",
    "    conf = Config3D (\n",
    "        rays=rays,\n",
    "        grid=grid,\n",
    "        anisotropy=anisotropy,\n",
    "        use_gpu=use_gpu,\n",
    "        n_channel_in=n_channel,\n",
    "        # adjust for your data below (make patch size as large as possible)\n",
    "        train_patch_size=train_patch_size,\n",
    "        train_batch_size=1,\n",
    "    )\n",
    "    vars(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using default values: prob_thresh=0.5, nms_thresh=0.4.\n",
      "Train for 100 steps, validate on 1 samples\n",
      "Epoch 1/400\n",
      "100/100 [==============================] - 180s 2s/step - loss: 1.3974 - prob_loss: 0.4498 - dist_loss: 4.7378 - prob_kld: 0.3748 - dist_relevant_mae: 4.7376 - dist_relevant_mse: 37.4066 - val_loss: 1.0447 - val_prob_loss: 0.3396 - val_dist_loss: 3.5253 - val_prob_kld: 0.2915 - val_dist_relevant_mae: 3.5251 - val_dist_relevant_mse: 30.3693\n",
      "Epoch 2/400\n",
      "100/100 [==============================] - 174s 2s/step - loss: 1.0183 - prob_loss: 0.2556 - dist_loss: 3.8133 - prob_kld: 0.1845 - dist_relevant_mae: 3.8129 - dist_relevant_mse: 26.4745 - val_loss: 0.8500 - val_prob_loss: 0.0804 - val_dist_loss: 3.8478 - val_prob_kld: 0.0328 - val_dist_relevant_mae: 3.8475 - val_dist_relevant_mse: 27.5016\n",
      "Epoch 3/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.7354 - prob_loss: 0.1277 - dist_loss: 3.0384 - prob_kld: 0.0532 - dist_relevant_mae: 3.0379 - dist_relevant_mse: 17.4642 - val_loss: 0.8335 - val_prob_loss: 0.1188 - val_dist_loss: 3.5734 - val_prob_kld: 0.0712 - val_dist_relevant_mae: 3.5732 - val_dist_relevant_mse: 25.3128\n",
      "Epoch 4/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.7390 - prob_loss: 0.1194 - dist_loss: 3.0983 - prob_kld: 0.0513 - dist_relevant_mae: 3.0979 - dist_relevant_mse: 18.2997 - val_loss: 0.6410 - val_prob_loss: 0.0647 - val_dist_loss: 2.8815 - val_prob_kld: 0.0171 - val_dist_relevant_mae: 2.8810 - val_dist_relevant_mse: 17.4203\n",
      "Epoch 5/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.6219 - prob_loss: 0.1045 - dist_loss: 2.5872 - prob_kld: 0.0327 - dist_relevant_mae: 2.5868 - dist_relevant_mse: 13.8669 - val_loss: 0.5477 - val_prob_loss: 0.0635 - val_dist_loss: 2.4209 - val_prob_kld: 0.0159 - val_dist_relevant_mae: 2.4206 - val_dist_relevant_mse: 12.2218\n",
      "Epoch 6/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.5563 - prob_loss: 0.1046 - dist_loss: 2.2589 - prob_kld: 0.0315 - dist_relevant_mae: 2.2585 - dist_relevant_mse: 11.5087 - val_loss: 0.4689 - val_prob_loss: 0.0642 - val_dist_loss: 2.0236 - val_prob_kld: 0.0166 - val_dist_relevant_mae: 2.0233 - val_dist_relevant_mse: 10.6754\n",
      "Epoch 7/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.5251 - prob_loss: 0.0973 - dist_loss: 2.1393 - prob_kld: 0.0297 - dist_relevant_mae: 2.1389 - dist_relevant_mse: 10.6764 - val_loss: 0.4462 - val_prob_loss: 0.0644 - val_dist_loss: 1.9088 - val_prob_kld: 0.0169 - val_dist_relevant_mae: 1.9084 - val_dist_relevant_mse: 9.9100\n",
      "Epoch 8/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4690 - prob_loss: 0.0980 - dist_loss: 1.8554 - prob_kld: 0.0298 - dist_relevant_mae: 1.8550 - dist_relevant_mse: 8.7014 - val_loss: 0.4309 - val_prob_loss: 0.0647 - val_dist_loss: 1.8308 - val_prob_kld: 0.0172 - val_dist_relevant_mae: 1.8305 - val_dist_relevant_mse: 9.2508\n",
      "Epoch 9/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.4506 - prob_loss: 0.0849 - dist_loss: 1.8282 - prob_kld: 0.0218 - dist_relevant_mae: 1.8279 - dist_relevant_mse: 8.5632 - val_loss: 0.4011 - val_prob_loss: 0.0599 - val_dist_loss: 1.7059 - val_prob_kld: 0.0123 - val_dist_relevant_mae: 1.7056 - val_dist_relevant_mse: 7.9744\n",
      "Epoch 10/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4495 - prob_loss: 0.0992 - dist_loss: 1.7514 - prob_kld: 0.0270 - dist_relevant_mae: 1.7511 - dist_relevant_mse: 8.0947 - val_loss: 0.5045 - val_prob_loss: 0.0604 - val_dist_loss: 2.2204 - val_prob_kld: 0.0128 - val_dist_relevant_mae: 2.2202 - val_dist_relevant_mse: 11.6043\n",
      "Epoch 11/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4423 - prob_loss: 0.0892 - dist_loss: 1.7659 - prob_kld: 0.0212 - dist_relevant_mae: 1.7655 - dist_relevant_mse: 8.0220 - val_loss: 0.3824 - val_prob_loss: 0.0586 - val_dist_loss: 1.6193 - val_prob_kld: 0.0110 - val_dist_relevant_mae: 1.6189 - val_dist_relevant_mse: 7.6459\n",
      "Epoch 12/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4685 - prob_loss: 0.0967 - dist_loss: 1.8593 - prob_kld: 0.0246 - dist_relevant_mae: 1.8589 - dist_relevant_mse: 8.8274 - val_loss: 0.3857 - val_prob_loss: 0.0594 - val_dist_loss: 1.6317 - val_prob_kld: 0.0118 - val_dist_relevant_mae: 1.6313 - val_dist_relevant_mse: 7.2428\n",
      "Epoch 13/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4053 - prob_loss: 0.0915 - dist_loss: 1.5691 - prob_kld: 0.0182 - dist_relevant_mae: 1.5688 - dist_relevant_mse: 7.0342 - val_loss: 0.3683 - val_prob_loss: 0.0573 - val_dist_loss: 1.5552 - val_prob_kld: 0.0097 - val_dist_relevant_mae: 1.5548 - val_dist_relevant_mse: 7.0426\n",
      "Epoch 14/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.4164 - prob_loss: 0.0976 - dist_loss: 1.5938 - prob_kld: 0.0215 - dist_relevant_mae: 1.5934 - dist_relevant_mse: 6.9874 - val_loss: 0.3524 - val_prob_loss: 0.0591 - val_dist_loss: 1.4666 - val_prob_kld: 0.0115 - val_dist_relevant_mae: 1.4662 - val_dist_relevant_mse: 6.0395\n",
      "Epoch 15/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3997 - prob_loss: 0.0915 - dist_loss: 1.5410 - prob_kld: 0.0209 - dist_relevant_mae: 1.5406 - dist_relevant_mse: 6.5448 - val_loss: 0.3395 - val_prob_loss: 0.0576 - val_dist_loss: 1.4093 - val_prob_kld: 0.0100 - val_dist_relevant_mae: 1.4090 - val_dist_relevant_mse: 6.0853\n",
      "Epoch 16/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4011 - prob_loss: 0.0950 - dist_loss: 1.5308 - prob_kld: 0.0208 - dist_relevant_mae: 1.5305 - dist_relevant_mse: 6.3760 - val_loss: 0.3783 - val_prob_loss: 0.0569 - val_dist_loss: 1.6066 - val_prob_kld: 0.0093 - val_dist_relevant_mae: 1.6062 - val_dist_relevant_mse: 6.2677\n",
      "Epoch 17/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3801 - prob_loss: 0.0962 - dist_loss: 1.4194 - prob_kld: 0.0232 - dist_relevant_mae: 1.4191 - dist_relevant_mse: 5.7844 - val_loss: 0.3162 - val_prob_loss: 0.0571 - val_dist_loss: 1.2955 - val_prob_kld: 0.0096 - val_dist_relevant_mae: 1.2951 - val_dist_relevant_mse: 4.9575\n",
      "Epoch 18/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3694 - prob_loss: 0.0841 - dist_loss: 1.4261 - prob_kld: 0.0160 - dist_relevant_mae: 1.4258 - dist_relevant_mse: 5.6596 - val_loss: 0.3341 - val_prob_loss: 0.0570 - val_dist_loss: 1.3859 - val_prob_kld: 0.0094 - val_dist_relevant_mae: 1.3855 - val_dist_relevant_mse: 5.7639\n",
      "Epoch 19/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3628 - prob_loss: 0.0837 - dist_loss: 1.3953 - prob_kld: 0.0157 - dist_relevant_mae: 1.3950 - dist_relevant_mse: 5.4402 - val_loss: 0.3147 - val_prob_loss: 0.0560 - val_dist_loss: 1.2933 - val_prob_kld: 0.0084 - val_dist_relevant_mae: 1.2930 - val_dist_relevant_mse: 4.9725\n",
      "Epoch 20/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3574 - prob_loss: 0.0921 - dist_loss: 1.3266 - prob_kld: 0.0188 - dist_relevant_mae: 1.3263 - dist_relevant_mse: 5.1304 - val_loss: 0.3106 - val_prob_loss: 0.0626 - val_dist_loss: 1.2398 - val_prob_kld: 0.0151 - val_dist_relevant_mae: 1.2395 - val_dist_relevant_mse: 4.6361\n",
      "Epoch 21/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3596 - prob_loss: 0.0948 - dist_loss: 1.3238 - prob_kld: 0.0178 - dist_relevant_mae: 1.3235 - dist_relevant_mse: 5.0353 - val_loss: 0.3029 - val_prob_loss: 0.0555 - val_dist_loss: 1.2370 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 1.2367 - val_dist_relevant_mse: 4.6238\n",
      "Epoch 22/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3536 - prob_loss: 0.0893 - dist_loss: 1.3215 - prob_kld: 0.0173 - dist_relevant_mae: 1.3212 - dist_relevant_mse: 5.0423 - val_loss: 0.3070 - val_prob_loss: 0.0557 - val_dist_loss: 1.2565 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.2562 - val_dist_relevant_mse: 4.9686\n",
      "Epoch 23/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3440 - prob_loss: 0.0858 - dist_loss: 1.2908 - prob_kld: 0.0165 - dist_relevant_mae: 1.2904 - dist_relevant_mse: 4.9565 - val_loss: 0.2973 - val_prob_loss: 0.0603 - val_dist_loss: 1.1853 - val_prob_kld: 0.0127 - val_dist_relevant_mae: 1.1849 - val_dist_relevant_mse: 4.1860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3540 - prob_loss: 0.0924 - dist_loss: 1.3081 - prob_kld: 0.0210 - dist_relevant_mae: 1.3078 - dist_relevant_mse: 4.7722 - val_loss: 0.3083 - val_prob_loss: 0.0568 - val_dist_loss: 1.2574 - val_prob_kld: 0.0092 - val_dist_relevant_mae: 1.2570 - val_dist_relevant_mse: 4.5659\n",
      "Epoch 25/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3576 - prob_loss: 0.0958 - dist_loss: 1.3091 - prob_kld: 0.0172 - dist_relevant_mae: 1.3087 - dist_relevant_mse: 4.8782 - val_loss: 0.2939 - val_prob_loss: 0.0566 - val_dist_loss: 1.1865 - val_prob_kld: 0.0091 - val_dist_relevant_mae: 1.1862 - val_dist_relevant_mse: 4.2111\n",
      "Epoch 26/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3388 - prob_loss: 0.0878 - dist_loss: 1.2550 - prob_kld: 0.0154 - dist_relevant_mae: 1.2547 - dist_relevant_mse: 4.6979 - val_loss: 0.2960 - val_prob_loss: 0.0554 - val_dist_loss: 1.2027 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.2024 - val_dist_relevant_mse: 4.0218\n",
      "Epoch 27/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3499 - prob_loss: 0.0877 - dist_loss: 1.3109 - prob_kld: 0.0159 - dist_relevant_mae: 1.3106 - dist_relevant_mse: 4.8775 - val_loss: 0.3353 - val_prob_loss: 0.0554 - val_dist_loss: 1.3995 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.3991 - val_dist_relevant_mse: 4.4963\n",
      "Epoch 28/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3382 - prob_loss: 0.0892 - dist_loss: 1.2453 - prob_kld: 0.0181 - dist_relevant_mae: 1.2450 - dist_relevant_mse: 4.5739 - val_loss: 0.2800 - val_prob_loss: 0.0553 - val_dist_loss: 1.1238 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.1235 - val_dist_relevant_mse: 3.9281\n",
      "Epoch 29/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3154 - prob_loss: 0.0872 - dist_loss: 1.1407 - prob_kld: 0.0140 - dist_relevant_mae: 1.1404 - dist_relevant_mse: 4.1546 - val_loss: 0.2696 - val_prob_loss: 0.0554 - val_dist_loss: 1.0713 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.0711 - val_dist_relevant_mse: 3.6753\n",
      "Epoch 30/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3278 - prob_loss: 0.0838 - dist_loss: 1.2202 - prob_kld: 0.0143 - dist_relevant_mae: 1.2198 - dist_relevant_mse: 4.2696 - val_loss: 0.2794 - val_prob_loss: 0.0550 - val_dist_loss: 1.1217 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.1214 - val_dist_relevant_mse: 3.9387\n",
      "Epoch 31/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3245 - prob_loss: 0.0812 - dist_loss: 1.2167 - prob_kld: 0.0146 - dist_relevant_mae: 1.2164 - dist_relevant_mse: 4.3616 - val_loss: 0.2931 - val_prob_loss: 0.0570 - val_dist_loss: 1.1806 - val_prob_kld: 0.0094 - val_dist_relevant_mae: 1.1803 - val_dist_relevant_mse: 3.9743\n",
      "Epoch 32/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3339 - prob_loss: 0.0900 - dist_loss: 1.2196 - prob_kld: 0.0189 - dist_relevant_mae: 1.2193 - dist_relevant_mse: 4.3537 - val_loss: 0.2703 - val_prob_loss: 0.0550 - val_dist_loss: 1.0763 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.0760 - val_dist_relevant_mse: 3.7133\n",
      "Epoch 33/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.3231 - prob_loss: 0.0861 - dist_loss: 1.1850 - prob_kld: 0.0144 - dist_relevant_mae: 1.1847 - dist_relevant_mse: 4.2269 - val_loss: 0.3069 - val_prob_loss: 0.0571 - val_dist_loss: 1.2491 - val_prob_kld: 0.0095 - val_dist_relevant_mae: 1.2487 - val_dist_relevant_mse: 4.2080\n",
      "Epoch 34/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3254 - prob_loss: 0.0900 - dist_loss: 1.1771 - prob_kld: 0.0151 - dist_relevant_mae: 1.1768 - dist_relevant_mse: 4.2120 - val_loss: 0.2706 - val_prob_loss: 0.0557 - val_dist_loss: 1.0749 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.0746 - val_dist_relevant_mse: 3.4680\n",
      "Epoch 35/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3265 - prob_loss: 0.0874 - dist_loss: 1.1955 - prob_kld: 0.0165 - dist_relevant_mae: 1.1952 - dist_relevant_mse: 4.2011 - val_loss: 0.3023 - val_prob_loss: 0.0609 - val_dist_loss: 1.2072 - val_prob_kld: 0.0133 - val_dist_relevant_mae: 1.2069 - val_dist_relevant_mse: 4.0509\n",
      "Epoch 36/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3143 - prob_loss: 0.0863 - dist_loss: 1.1399 - prob_kld: 0.0148 - dist_relevant_mae: 1.1396 - dist_relevant_mse: 4.0126 - val_loss: 0.2752 - val_prob_loss: 0.0553 - val_dist_loss: 1.0995 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.0991 - val_dist_relevant_mse: 3.4919\n",
      "Epoch 37/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2991 - prob_loss: 0.0816 - dist_loss: 1.0874 - prob_kld: 0.0143 - dist_relevant_mae: 1.0871 - dist_relevant_mse: 3.6914 - val_loss: 0.2632 - val_prob_loss: 0.0550 - val_dist_loss: 1.0405 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.0402 - val_dist_relevant_mse: 3.4500\n",
      "Epoch 38/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.3103 - prob_loss: 0.0814 - dist_loss: 1.1443 - prob_kld: 0.0134 - dist_relevant_mae: 1.1440 - dist_relevant_mse: 3.9585 - val_loss: 0.2621 - val_prob_loss: 0.0550 - val_dist_loss: 1.0352 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.0349 - val_dist_relevant_mse: 3.3938\n",
      "Epoch 39/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3075 - prob_loss: 0.0862 - dist_loss: 1.1061 - prob_kld: 0.0145 - dist_relevant_mae: 1.1058 - dist_relevant_mse: 3.7486 - val_loss: 0.2598 - val_prob_loss: 0.0549 - val_dist_loss: 1.0247 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.0244 - val_dist_relevant_mse: 3.2276\n",
      "Epoch 40/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3169 - prob_loss: 0.0877 - dist_loss: 1.1459 - prob_kld: 0.0157 - dist_relevant_mae: 1.1456 - dist_relevant_mse: 3.9097 - val_loss: 0.2866 - val_prob_loss: 0.0564 - val_dist_loss: 1.1509 - val_prob_kld: 0.0089 - val_dist_relevant_mae: 1.1505 - val_dist_relevant_mse: 3.6666\n",
      "Epoch 41/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3148 - prob_loss: 0.0830 - dist_loss: 1.1591 - prob_kld: 0.0153 - dist_relevant_mae: 1.1588 - dist_relevant_mse: 3.9452 - val_loss: 0.2600 - val_prob_loss: 0.0547 - val_dist_loss: 1.0268 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.0265 - val_dist_relevant_mse: 3.2246\n",
      "Epoch 42/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3008 - prob_loss: 0.0857 - dist_loss: 1.0756 - prob_kld: 0.0145 - dist_relevant_mae: 1.0753 - dist_relevant_mse: 3.6249 - val_loss: 0.2511 - val_prob_loss: 0.0555 - val_dist_loss: 0.9780 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 0.9777 - val_dist_relevant_mse: 3.0943\n",
      "Epoch 43/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2927 - prob_loss: 0.0809 - dist_loss: 1.0592 - prob_kld: 0.0123 - dist_relevant_mae: 1.0589 - dist_relevant_mse: 3.5009 - val_loss: 0.2652 - val_prob_loss: 0.0544 - val_dist_loss: 1.0538 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0535 - val_dist_relevant_mse: 3.2380\n",
      "Epoch 44/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3050 - prob_loss: 0.0847 - dist_loss: 1.1015 - prob_kld: 0.0134 - dist_relevant_mae: 1.1012 - dist_relevant_mse: 3.7224 - val_loss: 0.2527 - val_prob_loss: 0.0543 - val_dist_loss: 0.9918 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.9915 - val_dist_relevant_mse: 3.2173\n",
      "Epoch 45/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2954 - prob_loss: 0.0860 - dist_loss: 1.0471 - prob_kld: 0.0144 - dist_relevant_mae: 1.0468 - dist_relevant_mse: 3.5213 - val_loss: 0.2582 - val_prob_loss: 0.0557 - val_dist_loss: 1.0126 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.0123 - val_dist_relevant_mse: 3.2912\n",
      "Epoch 46/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2944 - prob_loss: 0.0829 - dist_loss: 1.0576 - prob_kld: 0.0129 - dist_relevant_mae: 1.0573 - dist_relevant_mse: 3.4259 - val_loss: 0.2433 - val_prob_loss: 0.0543 - val_dist_loss: 0.9454 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.9451 - val_dist_relevant_mse: 2.9119\n",
      "Epoch 47/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.3013 - prob_loss: 0.0889 - dist_loss: 1.0620 - prob_kld: 0.0138 - dist_relevant_mae: 1.0617 - dist_relevant_mse: 3.4248 - val_loss: 0.2682 - val_prob_loss: 0.0545 - val_dist_loss: 1.0684 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0681 - val_dist_relevant_mse: 3.4346\n",
      "Epoch 48/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2940 - prob_loss: 0.0843 - dist_loss: 1.0483 - prob_kld: 0.0144 - dist_relevant_mae: 1.0479 - dist_relevant_mse: 3.3934 - val_loss: 0.2439 - val_prob_loss: 0.0553 - val_dist_loss: 0.9434 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 0.9431 - val_dist_relevant_mse: 2.9393\n",
      "Epoch 49/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2829 - prob_loss: 0.0834 - dist_loss: 0.9975 - prob_kld: 0.0131 - dist_relevant_mae: 0.9972 - dist_relevant_mse: 3.2390 - val_loss: 0.2460 - val_prob_loss: 0.0554 - val_dist_loss: 0.9531 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 0.9528 - val_dist_relevant_mse: 2.9429\n",
      "Epoch 50/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2780 - prob_loss: 0.0819 - dist_loss: 0.9807 - prob_kld: 0.0122 - dist_relevant_mae: 0.9804 - dist_relevant_mse: 3.1705 - val_loss: 0.2498 - val_prob_loss: 0.0549 - val_dist_loss: 0.9745 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 0.9742 - val_dist_relevant_mse: 3.0430\n",
      "Epoch 51/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2863 - prob_loss: 0.0854 - dist_loss: 1.0044 - prob_kld: 0.0130 - dist_relevant_mae: 1.0042 - dist_relevant_mse: 3.2213 - val_loss: 0.2478 - val_prob_loss: 0.0544 - val_dist_loss: 0.9669 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 0.9667 - val_dist_relevant_mse: 2.8125\n",
      "Epoch 52/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2769 - prob_loss: 0.0807 - dist_loss: 0.9814 - prob_kld: 0.0124 - dist_relevant_mae: 0.9811 - dist_relevant_mse: 3.1513 - val_loss: 0.2378 - val_prob_loss: 0.0548 - val_dist_loss: 0.9151 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 0.9148 - val_dist_relevant_mse: 2.7676\n",
      "Epoch 53/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2743 - prob_loss: 0.0801 - dist_loss: 0.9708 - prob_kld: 0.0115 - dist_relevant_mae: 0.9705 - dist_relevant_mse: 3.0920 - val_loss: 0.2512 - val_prob_loss: 0.0546 - val_dist_loss: 0.9826 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.9823 - val_dist_relevant_mse: 3.0456\n",
      "Epoch 54/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2930 - prob_loss: 0.0858 - dist_loss: 1.0362 - prob_kld: 0.0147 - dist_relevant_mae: 1.0358 - dist_relevant_mse: 3.3091 - val_loss: 0.2548 - val_prob_loss: 0.0634 - val_dist_loss: 0.9569 - val_prob_kld: 0.0158 - val_dist_relevant_mae: 0.9566 - val_dist_relevant_mse: 2.7804\n",
      "Epoch 55/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.3031 - prob_loss: 0.0854 - dist_loss: 1.0882 - prob_kld: 0.0158 - dist_relevant_mae: 1.0879 - dist_relevant_mse: 3.5012 - val_loss: 0.2651 - val_prob_loss: 0.0552 - val_dist_loss: 1.0492 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.0489 - val_dist_relevant_mse: 3.3343\n",
      "Epoch 56/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2784 - prob_loss: 0.0767 - dist_loss: 1.0083 - prob_kld: 0.0126 - dist_relevant_mae: 1.0080 - dist_relevant_mse: 3.1531 - val_loss: 0.2369 - val_prob_loss: 0.0541 - val_dist_loss: 0.9142 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.9139 - val_dist_relevant_mse: 2.7486\n",
      "Epoch 57/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2726 - prob_loss: 0.0806 - dist_loss: 0.9602 - prob_kld: 0.0115 - dist_relevant_mae: 0.9598 - dist_relevant_mse: 2.9697 - val_loss: 0.2334 - val_prob_loss: 0.0540 - val_dist_loss: 0.8972 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.8970 - val_dist_relevant_mse: 2.6285\n",
      "Epoch 58/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2706 - prob_loss: 0.0755 - dist_loss: 0.9752 - prob_kld: 0.0120 - dist_relevant_mae: 0.9749 - dist_relevant_mse: 3.0348 - val_loss: 0.2511 - val_prob_loss: 0.0544 - val_dist_loss: 0.9831 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 0.9828 - val_dist_relevant_mse: 3.0759\n",
      "Epoch 59/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2916 - prob_loss: 0.0947 - dist_loss: 0.9846 - prob_kld: 0.0152 - dist_relevant_mae: 0.9842 - dist_relevant_mse: 3.1217 - val_loss: 0.2530 - val_prob_loss: 0.0658 - val_dist_loss: 0.9359 - val_prob_kld: 0.0182 - val_dist_relevant_mae: 0.9356 - val_dist_relevant_mse: 2.7201\n",
      "Epoch 60/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2792 - prob_loss: 0.0797 - dist_loss: 0.9974 - prob_kld: 0.0127 - dist_relevant_mae: 0.9970 - dist_relevant_mse: 3.0980 - val_loss: 0.2387 - val_prob_loss: 0.0543 - val_dist_loss: 0.9220 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.9217 - val_dist_relevant_mse: 2.7109\n",
      "Epoch 61/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2740 - prob_loss: 0.0804 - dist_loss: 0.9682 - prob_kld: 0.0121 - dist_relevant_mae: 0.9679 - dist_relevant_mse: 2.9995 - val_loss: 0.2384 - val_prob_loss: 0.0551 - val_dist_loss: 0.9164 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 0.9161 - val_dist_relevant_mse: 2.7227\n",
      "Epoch 62/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2823 - prob_loss: 0.0821 - dist_loss: 1.0012 - prob_kld: 0.0121 - dist_relevant_mae: 1.0008 - dist_relevant_mse: 3.1254 - val_loss: 0.2468 - val_prob_loss: 0.0546 - val_dist_loss: 0.9612 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.9609 - val_dist_relevant_mse: 2.7407\n",
      "Epoch 63/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2730 - prob_loss: 0.0816 - dist_loss: 0.9570 - prob_kld: 0.0117 - dist_relevant_mae: 0.9567 - dist_relevant_mse: 2.9302 - val_loss: 0.2337 - val_prob_loss: 0.0563 - val_dist_loss: 0.8869 - val_prob_kld: 0.0087 - val_dist_relevant_mae: 0.8866 - val_dist_relevant_mse: 2.5348\n",
      "Epoch 64/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2828 - prob_loss: 0.0790 - dist_loss: 1.0194 - prob_kld: 0.0118 - dist_relevant_mae: 1.0191 - dist_relevant_mse: 3.1743 - val_loss: 0.2373 - val_prob_loss: 0.0545 - val_dist_loss: 0.9141 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 0.9138 - val_dist_relevant_mse: 2.7717\n",
      "Epoch 65/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2723 - prob_loss: 0.0797 - dist_loss: 0.9633 - prob_kld: 0.0118 - dist_relevant_mae: 0.9630 - dist_relevant_mse: 2.9965 - val_loss: 0.2324 - val_prob_loss: 0.0542 - val_dist_loss: 0.8907 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.8904 - val_dist_relevant_mse: 2.6087\n",
      "Epoch 66/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2719 - prob_loss: 0.0764 - dist_loss: 0.9776 - prob_kld: 0.0111 - dist_relevant_mae: 0.9773 - dist_relevant_mse: 2.9529 - val_loss: 0.2372 - val_prob_loss: 0.0546 - val_dist_loss: 0.9130 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.9127 - val_dist_relevant_mse: 2.7112\n",
      "Epoch 67/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2716 - prob_loss: 0.0791 - dist_loss: 0.9628 - prob_kld: 0.0116 - dist_relevant_mae: 0.9625 - dist_relevant_mse: 2.9452 - val_loss: 0.2301 - val_prob_loss: 0.0554 - val_dist_loss: 0.8737 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 0.8734 - val_dist_relevant_mse: 2.5507\n",
      "Epoch 68/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2693 - prob_loss: 0.0804 - dist_loss: 0.9444 - prob_kld: 0.0125 - dist_relevant_mae: 0.9441 - dist_relevant_mse: 2.8991 - val_loss: 0.2320 - val_prob_loss: 0.0542 - val_dist_loss: 0.8892 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.8889 - val_dist_relevant_mse: 2.5597\n",
      "Epoch 69/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2683 - prob_loss: 0.0837 - dist_loss: 0.9232 - prob_kld: 0.0135 - dist_relevant_mae: 0.9229 - dist_relevant_mse: 2.7613 - val_loss: 0.2326 - val_prob_loss: 0.0552 - val_dist_loss: 0.8869 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 0.8866 - val_dist_relevant_mse: 2.5614\n",
      "Epoch 70/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 171s 2s/step - loss: 0.2652 - prob_loss: 0.0778 - dist_loss: 0.9369 - prob_kld: 0.0124 - dist_relevant_mae: 0.9366 - dist_relevant_mse: 2.8902 - val_loss: 0.2352 - val_prob_loss: 0.0552 - val_dist_loss: 0.8998 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 0.8995 - val_dist_relevant_mse: 2.4609\n",
      "Epoch 71/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2741 - prob_loss: 0.0832 - dist_loss: 0.9544 - prob_kld: 0.0124 - dist_relevant_mae: 0.9541 - dist_relevant_mse: 2.9012 - val_loss: 0.2292 - val_prob_loss: 0.0545 - val_dist_loss: 0.8730 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.8728 - val_dist_relevant_mse: 2.4966\n",
      "Epoch 72/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2729 - prob_loss: 0.0840 - dist_loss: 0.9445 - prob_kld: 0.0131 - dist_relevant_mae: 0.9442 - dist_relevant_mse: 2.8700 - val_loss: 0.2302 - val_prob_loss: 0.0546 - val_dist_loss: 0.8779 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.8776 - val_dist_relevant_mse: 2.5717\n",
      "Epoch 73/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2652 - prob_loss: 0.0784 - dist_loss: 0.9341 - prob_kld: 0.0116 - dist_relevant_mae: 0.9337 - dist_relevant_mse: 2.7998 - val_loss: 0.2412 - val_prob_loss: 0.0631 - val_dist_loss: 0.8906 - val_prob_kld: 0.0155 - val_dist_relevant_mae: 0.8903 - val_dist_relevant_mse: 2.5432\n",
      "Epoch 74/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2733 - prob_loss: 0.0849 - dist_loss: 0.9418 - prob_kld: 0.0145 - dist_relevant_mae: 0.9414 - dist_relevant_mse: 2.8682 - val_loss: 0.2237 - val_prob_loss: 0.0538 - val_dist_loss: 0.8492 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8489 - val_dist_relevant_mse: 2.3488\n",
      "Epoch 75/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2732 - prob_loss: 0.0842 - dist_loss: 0.9448 - prob_kld: 0.0117 - dist_relevant_mae: 0.9444 - dist_relevant_mse: 2.8403 - val_loss: 0.2277 - val_prob_loss: 0.0538 - val_dist_loss: 0.8693 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8689 - val_dist_relevant_mse: 2.3845\n",
      "Epoch 76/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2615 - prob_loss: 0.0794 - dist_loss: 0.9102 - prob_kld: 0.0112 - dist_relevant_mae: 0.9099 - dist_relevant_mse: 2.7350 - val_loss: 0.2204 - val_prob_loss: 0.0538 - val_dist_loss: 0.8333 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8330 - val_dist_relevant_mse: 2.3344\n",
      "Epoch 77/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2695 - prob_loss: 0.0838 - dist_loss: 0.9285 - prob_kld: 0.0123 - dist_relevant_mae: 0.9282 - dist_relevant_mse: 2.7138 - val_loss: 0.2274 - val_prob_loss: 0.0542 - val_dist_loss: 0.8658 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.8655 - val_dist_relevant_mse: 2.3328\n",
      "Epoch 78/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2808 - prob_loss: 0.0853 - dist_loss: 0.9776 - prob_kld: 0.0168 - dist_relevant_mae: 0.9773 - dist_relevant_mse: 2.9365 - val_loss: 0.2193 - val_prob_loss: 0.0538 - val_dist_loss: 0.8276 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8273 - val_dist_relevant_mse: 2.2840\n",
      "Epoch 79/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2617 - prob_loss: 0.0792 - dist_loss: 0.9128 - prob_kld: 0.0105 - dist_relevant_mae: 0.9125 - dist_relevant_mse: 2.7314 - val_loss: 0.2230 - val_prob_loss: 0.0537 - val_dist_loss: 0.8465 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8462 - val_dist_relevant_mse: 2.3639\n",
      "Epoch 80/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2698 - prob_loss: 0.0883 - dist_loss: 0.9076 - prob_kld: 0.0122 - dist_relevant_mae: 0.9073 - dist_relevant_mse: 2.6884 - val_loss: 0.2235 - val_prob_loss: 0.0536 - val_dist_loss: 0.8491 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.8488 - val_dist_relevant_mse: 2.2939\n",
      "Epoch 81/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2661 - prob_loss: 0.0832 - dist_loss: 0.9146 - prob_kld: 0.0120 - dist_relevant_mae: 0.9142 - dist_relevant_mse: 2.6916 - val_loss: 0.2247 - val_prob_loss: 0.0548 - val_dist_loss: 0.8495 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 0.8492 - val_dist_relevant_mse: 2.2952\n",
      "Epoch 82/400\n",
      "100/100 [==============================] - 170s 2s/step - loss: 0.2854 - prob_loss: 0.0842 - dist_loss: 1.0063 - prob_kld: 0.0132 - dist_relevant_mae: 1.0059 - dist_relevant_mse: 3.0378 - val_loss: 0.2261 - val_prob_loss: 0.0539 - val_dist_loss: 0.8608 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8604 - val_dist_relevant_mse: 2.3855\n",
      "Epoch 83/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2665 - prob_loss: 0.0840 - dist_loss: 0.9126 - prob_kld: 0.0114 - dist_relevant_mae: 0.9122 - dist_relevant_mse: 2.6704 - val_loss: 0.2158 - val_prob_loss: 0.0536 - val_dist_loss: 0.8113 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.8110 - val_dist_relevant_mse: 2.2589\n",
      "Epoch 84/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2587 - prob_loss: 0.0802 - dist_loss: 0.8924 - prob_kld: 0.0108 - dist_relevant_mae: 0.8920 - dist_relevant_mse: 2.6168 - val_loss: 0.2246 - val_prob_loss: 0.0539 - val_dist_loss: 0.8531 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.8527 - val_dist_relevant_mse: 2.3578\n",
      "Epoch 85/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2522 - prob_loss: 0.0731 - dist_loss: 0.8959 - prob_kld: 0.0098 - dist_relevant_mae: 0.8955 - dist_relevant_mse: 2.5722 - val_loss: 0.2210 - val_prob_loss: 0.0540 - val_dist_loss: 0.8346 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.8343 - val_dist_relevant_mse: 2.3101\n",
      "Epoch 86/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2620 - prob_loss: 0.0861 - dist_loss: 0.8793 - prob_kld: 0.0109 - dist_relevant_mae: 0.8790 - dist_relevant_mse: 2.6304 - val_loss: 0.2346 - val_prob_loss: 0.0541 - val_dist_loss: 0.9021 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.9017 - val_dist_relevant_mse: 2.6060\n",
      "Epoch 87/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2654 - prob_loss: 0.0799 - dist_loss: 0.9274 - prob_kld: 0.0112 - dist_relevant_mae: 0.9270 - dist_relevant_mse: 2.7249 - val_loss: 0.2275 - val_prob_loss: 0.0540 - val_dist_loss: 0.8676 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.8672 - val_dist_relevant_mse: 2.3733\n",
      "Epoch 88/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2559 - prob_loss: 0.0830 - dist_loss: 0.8644 - prob_kld: 0.0111 - dist_relevant_mae: 0.8641 - dist_relevant_mse: 2.4531 - val_loss: 0.2197 - val_prob_loss: 0.0538 - val_dist_loss: 0.8296 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8293 - val_dist_relevant_mse: 2.2496\n",
      "Epoch 89/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2609 - prob_loss: 0.0774 - dist_loss: 0.9175 - prob_kld: 0.0105 - dist_relevant_mae: 0.9172 - dist_relevant_mse: 2.6858 - val_loss: 0.2397 - val_prob_loss: 0.0544 - val_dist_loss: 0.9268 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 0.9264 - val_dist_relevant_mse: 2.6574\n",
      "Epoch 90/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2600 - prob_loss: 0.0851 - dist_loss: 0.8747 - prob_kld: 0.0113 - dist_relevant_mae: 0.8744 - dist_relevant_mse: 2.5281 - val_loss: 0.2150 - val_prob_loss: 0.0535 - val_dist_loss: 0.8073 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.8070 - val_dist_relevant_mse: 2.2058\n",
      "Epoch 91/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2604 - prob_loss: 0.0862 - dist_loss: 0.8710 - prob_kld: 0.0114 - dist_relevant_mae: 0.8707 - dist_relevant_mse: 2.5881 - val_loss: 0.2282 - val_prob_loss: 0.0547 - val_dist_loss: 0.8678 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 0.8675 - val_dist_relevant_mse: 2.3210\n",
      "Epoch 92/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2588 - prob_loss: 0.0781 - dist_loss: 0.9033 - prob_kld: 0.0106 - dist_relevant_mae: 0.9030 - dist_relevant_mse: 2.6140 - val_loss: 0.2154 - val_prob_loss: 0.0536 - val_dist_loss: 0.8088 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.8085 - val_dist_relevant_mse: 2.2071\n",
      "Epoch 93/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2632 - prob_loss: 0.0789 - dist_loss: 0.9212 - prob_kld: 0.0113 - dist_relevant_mae: 0.9208 - dist_relevant_mse: 2.6631 - val_loss: 0.2351 - val_prob_loss: 0.0564 - val_dist_loss: 0.8933 - val_prob_kld: 0.0089 - val_dist_relevant_mae: 0.8930 - val_dist_relevant_mse: 2.5319\n",
      "Epoch 94/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2598 - prob_loss: 0.0757 - dist_loss: 0.9203 - prob_kld: 0.0107 - dist_relevant_mae: 0.9200 - dist_relevant_mse: 2.5850 - val_loss: 0.2199 - val_prob_loss: 0.0540 - val_dist_loss: 0.8292 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.8289 - val_dist_relevant_mse: 2.2566\n",
      "Epoch 95/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2502 - prob_loss: 0.0818 - dist_loss: 0.8419 - prob_kld: 0.0108 - dist_relevant_mae: 0.8416 - dist_relevant_mse: 2.3751 - val_loss: 0.2212 - val_prob_loss: 0.0538 - val_dist_loss: 0.8368 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8365 - val_dist_relevant_mse: 2.3067\n",
      "Epoch 96/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2527 - prob_loss: 0.0809 - dist_loss: 0.8592 - prob_kld: 0.0104 - dist_relevant_mae: 0.8589 - dist_relevant_mse: 2.4665 - val_loss: 0.2153 - val_prob_loss: 0.0536 - val_dist_loss: 0.8086 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.8083 - val_dist_relevant_mse: 2.2086\n",
      "Epoch 97/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2546 - prob_loss: 0.0825 - dist_loss: 0.8604 - prob_kld: 0.0111 - dist_relevant_mae: 0.8601 - dist_relevant_mse: 2.4891 - val_loss: 0.2148 - val_prob_loss: 0.0536 - val_dist_loss: 0.8056 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.8053 - val_dist_relevant_mse: 2.1397\n",
      "Epoch 98/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2529 - prob_loss: 0.0789 - dist_loss: 0.8700 - prob_kld: 0.0104 - dist_relevant_mae: 0.8697 - dist_relevant_mse: 2.5158 - val_loss: 0.2194 - val_prob_loss: 0.0538 - val_dist_loss: 0.8278 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8275 - val_dist_relevant_mse: 2.3036\n",
      "Epoch 99/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2572 - prob_loss: 0.0756 - dist_loss: 0.9078 - prob_kld: 0.0112 - dist_relevant_mae: 0.9075 - dist_relevant_mse: 2.5771 - val_loss: 0.2296 - val_prob_loss: 0.0545 - val_dist_loss: 0.8758 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 0.8755 - val_dist_relevant_mse: 2.4432\n",
      "Epoch 100/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2619 - prob_loss: 0.0847 - dist_loss: 0.8858 - prob_kld: 0.0110 - dist_relevant_mae: 0.8855 - dist_relevant_mse: 2.5363 - val_loss: 0.2159 - val_prob_loss: 0.0535 - val_dist_loss: 0.8119 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.8116 - val_dist_relevant_mse: 2.1493\n",
      "Epoch 101/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2567 - prob_loss: 0.0811 - dist_loss: 0.8780 - prob_kld: 0.0123 - dist_relevant_mae: 0.8776 - dist_relevant_mse: 2.4755 - val_loss: 0.2197 - val_prob_loss: 0.0539 - val_dist_loss: 0.8291 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8287 - val_dist_relevant_mse: 2.2441\n",
      "Epoch 102/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2521 - prob_loss: 0.0779 - dist_loss: 0.8710 - prob_kld: 0.0105 - dist_relevant_mae: 0.8707 - dist_relevant_mse: 2.5217 - val_loss: 0.2262 - val_prob_loss: 0.0539 - val_dist_loss: 0.8614 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8611 - val_dist_relevant_mse: 2.2900\n",
      "Epoch 103/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2572 - prob_loss: 0.0814 - dist_loss: 0.8790 - prob_kld: 0.0111 - dist_relevant_mae: 0.8787 - dist_relevant_mse: 2.5403 - val_loss: 0.2160 - val_prob_loss: 0.0545 - val_dist_loss: 0.8075 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.8072 - val_dist_relevant_mse: 2.1387\n",
      "Epoch 104/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2529 - prob_loss: 0.0798 - dist_loss: 0.8656 - prob_kld: 0.0103 - dist_relevant_mae: 0.8653 - dist_relevant_mse: 2.5036 - val_loss: 0.2296 - val_prob_loss: 0.0546 - val_dist_loss: 0.8752 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 0.8749 - val_dist_relevant_mse: 2.4466\n",
      "Epoch 105/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2531 - prob_loss: 0.0806 - dist_loss: 0.8624 - prob_kld: 0.0103 - dist_relevant_mae: 0.8621 - dist_relevant_mse: 2.4313 - val_loss: 0.2181 - val_prob_loss: 0.0537 - val_dist_loss: 0.8218 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8215 - val_dist_relevant_mse: 2.2406\n",
      "Epoch 106/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2583 - prob_loss: 0.0822 - dist_loss: 0.8804 - prob_kld: 0.0108 - dist_relevant_mae: 0.8801 - dist_relevant_mse: 2.5092 - val_loss: 0.2187 - val_prob_loss: 0.0539 - val_dist_loss: 0.8239 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8237 - val_dist_relevant_mse: 2.1655\n",
      "Epoch 107/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2650 - prob_loss: 0.0823 - dist_loss: 0.9138 - prob_kld: 0.0110 - dist_relevant_mae: 0.9135 - dist_relevant_mse: 2.6372 - val_loss: 0.2177 - val_prob_loss: 0.0538 - val_dist_loss: 0.8196 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8193 - val_dist_relevant_mse: 2.2177\n",
      "Epoch 108/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2566 - prob_loss: 0.0825 - dist_loss: 0.8704 - prob_kld: 0.0114 - dist_relevant_mae: 0.8701 - dist_relevant_mse: 2.5237 - val_loss: 0.2154 - val_prob_loss: 0.0536 - val_dist_loss: 0.8091 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.8088 - val_dist_relevant_mse: 2.1643\n",
      "Epoch 109/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2463 - prob_loss: 0.0801 - dist_loss: 0.8306 - prob_kld: 0.0098 - dist_relevant_mae: 0.8303 - dist_relevant_mse: 2.3428 - val_loss: 0.2099 - val_prob_loss: 0.0535 - val_dist_loss: 0.7818 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7815 - val_dist_relevant_mse: 2.0475\n",
      "Epoch 110/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2508 - prob_loss: 0.0828 - dist_loss: 0.8402 - prob_kld: 0.0103 - dist_relevant_mae: 0.8399 - dist_relevant_mse: 2.3592 - val_loss: 0.2206 - val_prob_loss: 0.0548 - val_dist_loss: 0.8289 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 0.8286 - val_dist_relevant_mse: 2.1597\n",
      "Epoch 111/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2495 - prob_loss: 0.0840 - dist_loss: 0.8276 - prob_kld: 0.0107 - dist_relevant_mae: 0.8273 - dist_relevant_mse: 2.3359 - val_loss: 0.2112 - val_prob_loss: 0.0536 - val_dist_loss: 0.7880 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7877 - val_dist_relevant_mse: 2.0762\n",
      "Epoch 112/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2456 - prob_loss: 0.0782 - dist_loss: 0.8372 - prob_kld: 0.0099 - dist_relevant_mae: 0.8369 - dist_relevant_mse: 2.3062 - val_loss: 0.2179 - val_prob_loss: 0.0545 - val_dist_loss: 0.8169 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 0.8166 - val_dist_relevant_mse: 2.1570\n",
      "Epoch 113/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2526 - prob_loss: 0.0766 - dist_loss: 0.8802 - prob_kld: 0.0111 - dist_relevant_mae: 0.8799 - dist_relevant_mse: 2.5429 - val_loss: 0.2201 - val_prob_loss: 0.0541 - val_dist_loss: 0.8303 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.8300 - val_dist_relevant_mse: 2.1980\n",
      "Epoch 114/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2447 - prob_loss: 0.0769 - dist_loss: 0.8391 - prob_kld: 0.0096 - dist_relevant_mae: 0.8388 - dist_relevant_mse: 2.3801 - val_loss: 0.2146 - val_prob_loss: 0.0535 - val_dist_loss: 0.8054 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.8051 - val_dist_relevant_mse: 2.1460\n",
      "Epoch 115/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2367 - prob_loss: 0.0752 - dist_loss: 0.8075 - prob_kld: 0.0094 - dist_relevant_mae: 0.8072 - dist_relevant_mse: 2.2386 - val_loss: 0.2375 - val_prob_loss: 0.0550 - val_dist_loss: 0.9125 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 0.9123 - val_dist_relevant_mse: 2.3385\n",
      "Epoch 116/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2526 - prob_loss: 0.0804 - dist_loss: 0.8608 - prob_kld: 0.0109 - dist_relevant_mae: 0.8605 - dist_relevant_mse: 2.3984 - val_loss: 0.2168 - val_prob_loss: 0.0558 - val_dist_loss: 0.8053 - val_prob_kld: 0.0082 - val_dist_relevant_mae: 0.8050 - val_dist_relevant_mse: 2.0830\n",
      "Epoch 117/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2588 - prob_loss: 0.0845 - dist_loss: 0.8715 - prob_kld: 0.0113 - dist_relevant_mae: 0.8712 - dist_relevant_mse: 2.4562 - val_loss: 0.2136 - val_prob_loss: 0.0547 - val_dist_loss: 0.7945 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 0.7942 - val_dist_relevant_mse: 2.0424\n",
      "Epoch 118/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2678 - prob_loss: 0.0840 - dist_loss: 0.9189 - prob_kld: 0.0118 - dist_relevant_mae: 0.9186 - dist_relevant_mse: 2.5781 - val_loss: 0.2250 - val_prob_loss: 0.0541 - val_dist_loss: 0.8547 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.8544 - val_dist_relevant_mse: 2.2949\n",
      "Epoch 119/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2475 - prob_loss: 0.0773 - dist_loss: 0.8512 - prob_kld: 0.0101 - dist_relevant_mae: 0.8508 - dist_relevant_mse: 2.3780 - val_loss: 0.2097 - val_prob_loss: 0.0535 - val_dist_loss: 0.7809 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7806 - val_dist_relevant_mse: 2.0162\n",
      "Epoch 120/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2447 - prob_loss: 0.0840 - dist_loss: 0.8036 - prob_kld: 0.0101 - dist_relevant_mae: 0.8033 - dist_relevant_mse: 2.2858 - val_loss: 0.2081 - val_prob_loss: 0.0537 - val_dist_loss: 0.7721 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7718 - val_dist_relevant_mse: 1.9930\n",
      "Epoch 121/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2469 - prob_loss: 0.0771 - dist_loss: 0.8490 - prob_kld: 0.0099 - dist_relevant_mae: 0.8487 - dist_relevant_mse: 2.3587 - val_loss: 0.2159 - val_prob_loss: 0.0540 - val_dist_loss: 0.8092 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.8089 - val_dist_relevant_mse: 2.1054\n",
      "Epoch 122/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2441 - prob_loss: 0.0750 - dist_loss: 0.8457 - prob_kld: 0.0096 - dist_relevant_mae: 0.8454 - dist_relevant_mse: 2.3324 - val_loss: 0.2248 - val_prob_loss: 0.0538 - val_dist_loss: 0.8549 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8546 - val_dist_relevant_mse: 2.2633\n",
      "Epoch 123/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2494 - prob_loss: 0.0793 - dist_loss: 0.8505 - prob_kld: 0.0099 - dist_relevant_mae: 0.8502 - dist_relevant_mse: 2.3651 - val_loss: 0.2276 - val_prob_loss: 0.0542 - val_dist_loss: 0.8671 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.8668 - val_dist_relevant_mse: 2.1922\n",
      "Epoch 124/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2521 - prob_loss: 0.0797 - dist_loss: 0.8622 - prob_kld: 0.0100 - dist_relevant_mae: 0.8619 - dist_relevant_mse: 2.4097 - val_loss: 0.2157 - val_prob_loss: 0.0535 - val_dist_loss: 0.8108 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.8105 - val_dist_relevant_mse: 2.1580\n",
      "Epoch 125/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2474 - prob_loss: 0.0759 - dist_loss: 0.8574 - prob_kld: 0.0098 - dist_relevant_mae: 0.8571 - dist_relevant_mse: 2.3748 - val_loss: 0.2123 - val_prob_loss: 0.0540 - val_dist_loss: 0.7915 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.7912 - val_dist_relevant_mse: 2.0277\n",
      "Epoch 126/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2432 - prob_loss: 0.0731 - dist_loss: 0.8506 - prob_kld: 0.0094 - dist_relevant_mae: 0.8503 - dist_relevant_mse: 2.3434 - val_loss: 0.2174 - val_prob_loss: 0.0540 - val_dist_loss: 0.8171 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.8168 - val_dist_relevant_mse: 2.1912\n",
      "Epoch 127/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2412 - prob_loss: 0.0759 - dist_loss: 0.8267 - prob_kld: 0.0097 - dist_relevant_mae: 0.8264 - dist_relevant_mse: 2.2861 - val_loss: 0.2189 - val_prob_loss: 0.0536 - val_dist_loss: 0.8268 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.8265 - val_dist_relevant_mse: 2.0358\n",
      "Epoch 128/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2448 - prob_loss: 0.0827 - dist_loss: 0.8105 - prob_kld: 0.0097 - dist_relevant_mae: 0.8102 - dist_relevant_mse: 2.2575 - val_loss: 0.2058 - val_prob_loss: 0.0534 - val_dist_loss: 0.7621 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7619 - val_dist_relevant_mse: 1.9582\n",
      "Epoch 129/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2392 - prob_loss: 0.0756 - dist_loss: 0.8181 - prob_kld: 0.0096 - dist_relevant_mae: 0.8178 - dist_relevant_mse: 2.2296 - val_loss: 0.2307 - val_prob_loss: 0.0553 - val_dist_loss: 0.8773 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 0.8770 - val_dist_relevant_mse: 2.1561\n",
      "Epoch 130/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2484 - prob_loss: 0.0821 - dist_loss: 0.8314 - prob_kld: 0.0103 - dist_relevant_mae: 0.8311 - dist_relevant_mse: 2.2789 - val_loss: 0.2082 - val_prob_loss: 0.0539 - val_dist_loss: 0.7718 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.7715 - val_dist_relevant_mse: 1.9641\n",
      "Epoch 131/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2413 - prob_loss: 0.0783 - dist_loss: 0.8150 - prob_kld: 0.0094 - dist_relevant_mae: 0.8147 - dist_relevant_mse: 2.2094 - val_loss: 0.2058 - val_prob_loss: 0.0534 - val_dist_loss: 0.7617 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7614 - val_dist_relevant_mse: 1.9130\n",
      "Epoch 132/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2444 - prob_loss: 0.0812 - dist_loss: 0.8160 - prob_kld: 0.0105 - dist_relevant_mae: 0.8157 - dist_relevant_mse: 2.2638 - val_loss: 0.2113 - val_prob_loss: 0.0540 - val_dist_loss: 0.7866 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.7863 - val_dist_relevant_mse: 2.0541\n",
      "Epoch 133/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2486 - prob_loss: 0.0807 - dist_loss: 0.8397 - prob_kld: 0.0112 - dist_relevant_mae: 0.8394 - dist_relevant_mse: 2.2831 - val_loss: 0.2144 - val_prob_loss: 0.0543 - val_dist_loss: 0.8007 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.8004 - val_dist_relevant_mse: 2.1153\n",
      "Epoch 134/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2429 - prob_loss: 0.0793 - dist_loss: 0.8178 - prob_kld: 0.0104 - dist_relevant_mae: 0.8175 - dist_relevant_mse: 2.2569 - val_loss: 0.2093 - val_prob_loss: 0.0543 - val_dist_loss: 0.7751 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.7748 - val_dist_relevant_mse: 2.0056\n",
      "Epoch 135/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2453 - prob_loss: 0.0822 - dist_loss: 0.8152 - prob_kld: 0.0103 - dist_relevant_mae: 0.8149 - dist_relevant_mse: 2.2367 - val_loss: 0.2051 - val_prob_loss: 0.0533 - val_dist_loss: 0.7590 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7587 - val_dist_relevant_mse: 1.9408\n",
      "Epoch 136/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2416 - prob_loss: 0.0802 - dist_loss: 0.8071 - prob_kld: 0.0099 - dist_relevant_mae: 0.8068 - dist_relevant_mse: 2.1822 - val_loss: 0.2170 - val_prob_loss: 0.0534 - val_dist_loss: 0.8175 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.8173 - val_dist_relevant_mse: 2.1660\n",
      "Epoch 137/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2472 - prob_loss: 0.0840 - dist_loss: 0.8160 - prob_kld: 0.0103 - dist_relevant_mae: 0.8157 - dist_relevant_mse: 2.2377 - val_loss: 0.2091 - val_prob_loss: 0.0534 - val_dist_loss: 0.7788 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7785 - val_dist_relevant_mse: 1.9167\n",
      "Epoch 138/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2363 - prob_loss: 0.0756 - dist_loss: 0.8037 - prob_kld: 0.0094 - dist_relevant_mae: 0.8034 - dist_relevant_mse: 2.1129 - val_loss: 0.2072 - val_prob_loss: 0.0539 - val_dist_loss: 0.7666 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.7664 - val_dist_relevant_mse: 1.9422\n",
      "Epoch 139/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 173s 2s/step - loss: 0.2410 - prob_loss: 0.0776 - dist_loss: 0.8171 - prob_kld: 0.0096 - dist_relevant_mae: 0.8168 - dist_relevant_mse: 2.2245 - val_loss: 0.2232 - val_prob_loss: 0.0552 - val_dist_loss: 0.8403 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 0.8401 - val_dist_relevant_mse: 2.1208\n",
      "Epoch 140/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2402 - prob_loss: 0.0798 - dist_loss: 0.8018 - prob_kld: 0.0099 - dist_relevant_mae: 0.8015 - dist_relevant_mse: 2.1721 - val_loss: 0.2092 - val_prob_loss: 0.0538 - val_dist_loss: 0.7768 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.7765 - val_dist_relevant_mse: 1.9383\n",
      "Epoch 141/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2506 - prob_loss: 0.0872 - dist_loss: 0.8170 - prob_kld: 0.0106 - dist_relevant_mae: 0.8167 - dist_relevant_mse: 2.2262 - val_loss: 0.2081 - val_prob_loss: 0.0533 - val_dist_loss: 0.7740 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7738 - val_dist_relevant_mse: 1.9447\n",
      "Epoch 142/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2423 - prob_loss: 0.0802 - dist_loss: 0.8103 - prob_kld: 0.0126 - dist_relevant_mae: 0.8100 - dist_relevant_mse: 2.2011 - val_loss: 0.2069 - val_prob_loss: 0.0534 - val_dist_loss: 0.7676 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7673 - val_dist_relevant_mse: 1.8994\n",
      "Epoch 143/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2413 - prob_loss: 0.0823 - dist_loss: 0.7947 - prob_kld: 0.0099 - dist_relevant_mae: 0.7945 - dist_relevant_mse: 2.1703 - val_loss: 0.2105 - val_prob_loss: 0.0536 - val_dist_loss: 0.7842 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7839 - val_dist_relevant_mse: 1.9661\n",
      "Epoch 144/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2347 - prob_loss: 0.0743 - dist_loss: 0.8024 - prob_kld: 0.0087 - dist_relevant_mae: 0.8021 - dist_relevant_mse: 2.1587 - val_loss: 0.2127 - val_prob_loss: 0.0545 - val_dist_loss: 0.7911 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 0.7908 - val_dist_relevant_mse: 1.9992\n",
      "Epoch 145/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2444 - prob_loss: 0.0785 - dist_loss: 0.8294 - prob_kld: 0.0100 - dist_relevant_mae: 0.8292 - dist_relevant_mse: 2.1905 - val_loss: 0.2054 - val_prob_loss: 0.0534 - val_dist_loss: 0.7602 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7599 - val_dist_relevant_mse: 1.8801\n",
      "Epoch 146/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2358 - prob_loss: 0.0791 - dist_loss: 0.7836 - prob_kld: 0.0094 - dist_relevant_mae: 0.7833 - dist_relevant_mse: 2.0669 - val_loss: 0.2042 - val_prob_loss: 0.0531 - val_dist_loss: 0.7554 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7551 - val_dist_relevant_mse: 1.9249\n",
      "Epoch 147/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2441 - prob_loss: 0.0794 - dist_loss: 0.8231 - prob_kld: 0.0102 - dist_relevant_mae: 0.8228 - dist_relevant_mse: 2.2536 - val_loss: 0.2045 - val_prob_loss: 0.0540 - val_dist_loss: 0.7524 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.7521 - val_dist_relevant_mse: 1.8649\n",
      "Epoch 148/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2464 - prob_loss: 0.0857 - dist_loss: 0.8033 - prob_kld: 0.0112 - dist_relevant_mae: 0.8030 - dist_relevant_mse: 2.1957 - val_loss: 0.2095 - val_prob_loss: 0.0542 - val_dist_loss: 0.7766 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.7763 - val_dist_relevant_mse: 1.9638\n",
      "Epoch 149/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2450 - prob_loss: 0.0779 - dist_loss: 0.8356 - prob_kld: 0.0102 - dist_relevant_mae: 0.8353 - dist_relevant_mse: 2.2651 - val_loss: 0.2150 - val_prob_loss: 0.0556 - val_dist_loss: 0.7969 - val_prob_kld: 0.0080 - val_dist_relevant_mae: 0.7966 - val_dist_relevant_mse: 2.0121\n",
      "Epoch 150/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2418 - prob_loss: 0.0796 - dist_loss: 0.8110 - prob_kld: 0.0098 - dist_relevant_mae: 0.8107 - dist_relevant_mse: 2.1781 - val_loss: 0.2136 - val_prob_loss: 0.0542 - val_dist_loss: 0.7973 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.7970 - val_dist_relevant_mse: 1.9928\n",
      "Epoch 151/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2594 - prob_loss: 0.0878 - dist_loss: 0.8581 - prob_kld: 0.0109 - dist_relevant_mae: 0.8578 - dist_relevant_mse: 2.3706 - val_loss: 0.2174 - val_prob_loss: 0.0538 - val_dist_loss: 0.8179 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.8176 - val_dist_relevant_mse: 1.9712\n",
      "Epoch 152/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2336 - prob_loss: 0.0752 - dist_loss: 0.7921 - prob_kld: 0.0090 - dist_relevant_mae: 0.7919 - dist_relevant_mse: 2.1308 - val_loss: 0.2011 - val_prob_loss: 0.0531 - val_dist_loss: 0.7401 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7398 - val_dist_relevant_mse: 1.8539\n",
      "Epoch 153/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2382 - prob_loss: 0.0830 - dist_loss: 0.7757 - prob_kld: 0.0097 - dist_relevant_mae: 0.7754 - dist_relevant_mse: 2.0135 - val_loss: 0.2047 - val_prob_loss: 0.0536 - val_dist_loss: 0.7555 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7552 - val_dist_relevant_mse: 1.8730\n",
      "Epoch 154/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2450 - prob_loss: 0.0777 - dist_loss: 0.8363 - prob_kld: 0.0100 - dist_relevant_mae: 0.8360 - dist_relevant_mse: 2.2219 - val_loss: 0.2122 - val_prob_loss: 0.0538 - val_dist_loss: 0.7925 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.7922 - val_dist_relevant_mse: 2.0249\n",
      "Epoch 155/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2405 - prob_loss: 0.0794 - dist_loss: 0.8055 - prob_kld: 0.0098 - dist_relevant_mae: 0.8052 - dist_relevant_mse: 2.1355 - val_loss: 0.2025 - val_prob_loss: 0.0536 - val_dist_loss: 0.7447 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7444 - val_dist_relevant_mse: 1.8436\n",
      "Epoch 156/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2401 - prob_loss: 0.0781 - dist_loss: 0.8101 - prob_kld: 0.0105 - dist_relevant_mae: 0.8098 - dist_relevant_mse: 2.1765 - val_loss: 0.2075 - val_prob_loss: 0.0540 - val_dist_loss: 0.7672 - val_prob_kld: 0.0064 - val_dist_relevant_mae: 0.7669 - val_dist_relevant_mse: 1.9103\n",
      "Epoch 157/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2465 - prob_loss: 0.0848 - dist_loss: 0.8083 - prob_kld: 0.0107 - dist_relevant_mae: 0.8079 - dist_relevant_mse: 2.1104 - val_loss: 0.2042 - val_prob_loss: 0.0534 - val_dist_loss: 0.7540 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7537 - val_dist_relevant_mse: 1.8502\n",
      "Epoch 158/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2495 - prob_loss: 0.0827 - dist_loss: 0.8341 - prob_kld: 0.0100 - dist_relevant_mae: 0.8337 - dist_relevant_mse: 2.2279 - val_loss: 0.2065 - val_prob_loss: 0.0536 - val_dist_loss: 0.7649 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7646 - val_dist_relevant_mse: 1.9109\n",
      "Epoch 159/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2357 - prob_loss: 0.0803 - dist_loss: 0.7769 - prob_kld: 0.0093 - dist_relevant_mae: 0.7766 - dist_relevant_mse: 2.0857 - val_loss: 0.2029 - val_prob_loss: 0.0533 - val_dist_loss: 0.7484 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7481 - val_dist_relevant_mse: 1.8739\n",
      "Epoch 160/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2397 - prob_loss: 0.0782 - dist_loss: 0.8071 - prob_kld: 0.0096 - dist_relevant_mae: 0.8068 - dist_relevant_mse: 2.1172 - val_loss: 0.2195 - val_prob_loss: 0.0538 - val_dist_loss: 0.8286 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.8283 - val_dist_relevant_mse: 2.0461\n",
      "Epoch 161/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2362 - prob_loss: 0.0792 - dist_loss: 0.7849 - prob_kld: 0.0093 - dist_relevant_mae: 0.7846 - dist_relevant_mse: 2.1084 - val_loss: 0.2057 - val_prob_loss: 0.0534 - val_dist_loss: 0.7617 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7614 - val_dist_relevant_mse: 1.8720\n",
      "Epoch 162/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2391 - prob_loss: 0.0811 - dist_loss: 0.7897 - prob_kld: 0.0094 - dist_relevant_mae: 0.7894 - dist_relevant_mse: 2.0724 - val_loss: 0.2157 - val_prob_loss: 0.0544 - val_dist_loss: 0.8069 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 0.8066 - val_dist_relevant_mse: 2.0290\n",
      "Epoch 163/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2376 - prob_loss: 0.0799 - dist_loss: 0.7888 - prob_kld: 0.0095 - dist_relevant_mae: 0.7885 - dist_relevant_mse: 2.1141 - val_loss: 0.2041 - val_prob_loss: 0.0536 - val_dist_loss: 0.7525 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7523 - val_dist_relevant_mse: 1.8236\n",
      "Epoch 164/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2353 - prob_loss: 0.0819 - dist_loss: 0.7667 - prob_kld: 0.0094 - dist_relevant_mae: 0.7664 - dist_relevant_mse: 2.0056 - val_loss: 0.1989 - val_prob_loss: 0.0533 - val_dist_loss: 0.7280 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7277 - val_dist_relevant_mse: 1.8009\n",
      "Epoch 165/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2336 - prob_loss: 0.0805 - dist_loss: 0.7652 - prob_kld: 0.0093 - dist_relevant_mae: 0.7649 - dist_relevant_mse: 2.0121 - val_loss: 0.2001 - val_prob_loss: 0.0536 - val_dist_loss: 0.7326 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7323 - val_dist_relevant_mse: 1.7858\n",
      "Epoch 166/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2378 - prob_loss: 0.0807 - dist_loss: 0.7854 - prob_kld: 0.0098 - dist_relevant_mae: 0.7851 - dist_relevant_mse: 2.0590 - val_loss: 0.2060 - val_prob_loss: 0.0537 - val_dist_loss: 0.7615 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7612 - val_dist_relevant_mse: 1.8908\n",
      "Epoch 167/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2362 - prob_loss: 0.0768 - dist_loss: 0.7971 - prob_kld: 0.0092 - dist_relevant_mae: 0.7968 - dist_relevant_mse: 2.1201 - val_loss: 0.2064 - val_prob_loss: 0.0536 - val_dist_loss: 0.7643 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7640 - val_dist_relevant_mse: 1.8478\n",
      "Epoch 168/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2423 - prob_loss: 0.0809 - dist_loss: 0.8069 - prob_kld: 0.0097 - dist_relevant_mae: 0.8066 - dist_relevant_mse: 2.1481 - val_loss: 0.2128 - val_prob_loss: 0.0544 - val_dist_loss: 0.7923 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 0.7920 - val_dist_relevant_mse: 2.0073\n",
      "Epoch 169/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2428 - prob_loss: 0.0849 - dist_loss: 0.7893 - prob_kld: 0.0100 - dist_relevant_mae: 0.7890 - dist_relevant_mse: 2.1124 - val_loss: 0.2009 - val_prob_loss: 0.0534 - val_dist_loss: 0.7377 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7374 - val_dist_relevant_mse: 1.8216\n",
      "Epoch 170/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2391 - prob_loss: 0.0800 - dist_loss: 0.7953 - prob_kld: 0.0093 - dist_relevant_mae: 0.7950 - dist_relevant_mse: 2.0871 - val_loss: 0.2048 - val_prob_loss: 0.0534 - val_dist_loss: 0.7572 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7569 - val_dist_relevant_mse: 1.8366\n",
      "Epoch 171/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2376 - prob_loss: 0.0852 - dist_loss: 0.7619 - prob_kld: 0.0095 - dist_relevant_mae: 0.7616 - dist_relevant_mse: 1.9910 - val_loss: 0.2011 - val_prob_loss: 0.0534 - val_dist_loss: 0.7385 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7382 - val_dist_relevant_mse: 1.7676\n",
      "Epoch 172/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2323 - prob_loss: 0.0784 - dist_loss: 0.7692 - prob_kld: 0.0089 - dist_relevant_mae: 0.7689 - dist_relevant_mse: 1.9732 - val_loss: 0.2043 - val_prob_loss: 0.0533 - val_dist_loss: 0.7550 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7547 - val_dist_relevant_mse: 1.8077\n",
      "Epoch 173/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2348 - prob_loss: 0.0807 - dist_loss: 0.7706 - prob_kld: 0.0091 - dist_relevant_mae: 0.7703 - dist_relevant_mse: 2.0006 - val_loss: 0.2047 - val_prob_loss: 0.0535 - val_dist_loss: 0.7558 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7555 - val_dist_relevant_mse: 1.8249\n",
      "Epoch 174/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2386 - prob_loss: 0.0803 - dist_loss: 0.7912 - prob_kld: 0.0102 - dist_relevant_mae: 0.7910 - dist_relevant_mse: 2.0379 - val_loss: 0.2028 - val_prob_loss: 0.0534 - val_dist_loss: 0.7471 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7469 - val_dist_relevant_mse: 1.8484\n",
      "Epoch 175/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2310 - prob_loss: 0.0786 - dist_loss: 0.7622 - prob_kld: 0.0090 - dist_relevant_mae: 0.7619 - dist_relevant_mse: 2.0103 - val_loss: 0.2097 - val_prob_loss: 0.0538 - val_dist_loss: 0.7793 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.7790 - val_dist_relevant_mse: 1.9214\n",
      "Epoch 176/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2340 - prob_loss: 0.0772 - dist_loss: 0.7840 - prob_kld: 0.0091 - dist_relevant_mae: 0.7837 - dist_relevant_mse: 2.0305 - val_loss: 0.2089 - val_prob_loss: 0.0535 - val_dist_loss: 0.7768 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7766 - val_dist_relevant_mse: 1.9866\n",
      "Epoch 177/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2407 - prob_loss: 0.0844 - dist_loss: 0.7813 - prob_kld: 0.0095 - dist_relevant_mae: 0.7810 - dist_relevant_mse: 2.0685 - val_loss: 0.2069 - val_prob_loss: 0.0535 - val_dist_loss: 0.7671 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7668 - val_dist_relevant_mse: 1.8896\n",
      "Epoch 178/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2443 - prob_loss: 0.0792 - dist_loss: 0.8254 - prob_kld: 0.0095 - dist_relevant_mae: 0.8251 - dist_relevant_mse: 2.1683 - val_loss: 0.2064 - val_prob_loss: 0.0535 - val_dist_loss: 0.7646 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7643 - val_dist_relevant_mse: 1.8547\n",
      "Epoch 179/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2388 - prob_loss: 0.0797 - dist_loss: 0.7952 - prob_kld: 0.0099 - dist_relevant_mae: 0.7949 - dist_relevant_mse: 2.0915 - val_loss: 0.2123 - val_prob_loss: 0.0537 - val_dist_loss: 0.7929 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7926 - val_dist_relevant_mse: 1.8935\n",
      "Epoch 180/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2282 - prob_loss: 0.0694 - dist_loss: 0.7944 - prob_kld: 0.0085 - dist_relevant_mae: 0.7941 - dist_relevant_mse: 2.0367 - val_loss: 0.2223 - val_prob_loss: 0.0543 - val_dist_loss: 0.8403 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.8400 - val_dist_relevant_mse: 2.0872\n",
      "Epoch 181/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2361 - prob_loss: 0.0786 - dist_loss: 0.7875 - prob_kld: 0.0095 - dist_relevant_mae: 0.7871 - dist_relevant_mse: 2.0629 - val_loss: 0.2007 - val_prob_loss: 0.0533 - val_dist_loss: 0.7372 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7369 - val_dist_relevant_mse: 1.7582\n",
      "Epoch 182/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2367 - prob_loss: 0.0762 - dist_loss: 0.8027 - prob_kld: 0.0090 - dist_relevant_mae: 0.8023 - dist_relevant_mse: 2.1149 - val_loss: 0.2064 - val_prob_loss: 0.0532 - val_dist_loss: 0.7658 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7654 - val_dist_relevant_mse: 1.9299\n",
      "Epoch 183/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2371 - prob_loss: 0.0836 - dist_loss: 0.7673 - prob_kld: 0.0094 - dist_relevant_mae: 0.7670 - dist_relevant_mse: 1.9913 - val_loss: 0.2082 - val_prob_loss: 0.0532 - val_dist_loss: 0.7747 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7744 - val_dist_relevant_mse: 1.8406\n",
      "Epoch 184/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2425 - prob_loss: 0.0827 - dist_loss: 0.7987 - prob_kld: 0.0101 - dist_relevant_mae: 0.7984 - dist_relevant_mse: 2.0637 - val_loss: 0.2044 - val_prob_loss: 0.0532 - val_dist_loss: 0.7557 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7555 - val_dist_relevant_mse: 1.8687\n",
      "Epoch 185/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2431 - prob_loss: 0.0831 - dist_loss: 0.7999 - prob_kld: 0.0101 - dist_relevant_mae: 0.7996 - dist_relevant_mse: 2.0462 - val_loss: 0.2085 - val_prob_loss: 0.0535 - val_dist_loss: 0.7748 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7745 - val_dist_relevant_mse: 1.8418\n",
      "Epoch 186/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2271 - prob_loss: 0.0723 - dist_loss: 0.7738 - prob_kld: 0.0084 - dist_relevant_mae: 0.7735 - dist_relevant_mse: 2.0132 - val_loss: 0.1987 - val_prob_loss: 0.0533 - val_dist_loss: 0.7270 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7267 - val_dist_relevant_mse: 1.7604\n",
      "Epoch 187/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2357 - prob_loss: 0.0813 - dist_loss: 0.7721 - prob_kld: 0.0092 - dist_relevant_mae: 0.7718 - dist_relevant_mse: 2.0313 - val_loss: 0.2014 - val_prob_loss: 0.0535 - val_dist_loss: 0.7395 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7392 - val_dist_relevant_mse: 1.7935\n",
      "Epoch 188/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2280 - prob_loss: 0.0771 - dist_loss: 0.7541 - prob_kld: 0.0087 - dist_relevant_mae: 0.7538 - dist_relevant_mse: 1.8946 - val_loss: 0.2076 - val_prob_loss: 0.0536 - val_dist_loss: 0.7698 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7695 - val_dist_relevant_mse: 1.8839\n",
      "Epoch 189/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2364 - prob_loss: 0.0809 - dist_loss: 0.7778 - prob_kld: 0.0092 - dist_relevant_mae: 0.7775 - dist_relevant_mse: 2.0430 - val_loss: 0.2047 - val_prob_loss: 0.0536 - val_dist_loss: 0.7557 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7554 - val_dist_relevant_mse: 1.8156\n",
      "Epoch 190/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2326 - prob_loss: 0.0774 - dist_loss: 0.7758 - prob_kld: 0.0091 - dist_relevant_mae: 0.7755 - dist_relevant_mse: 1.9525 - val_loss: 0.2008 - val_prob_loss: 0.0536 - val_dist_loss: 0.7362 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7359 - val_dist_relevant_mse: 1.7984\n",
      "Epoch 191/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2356 - prob_loss: 0.0845 - dist_loss: 0.7554 - prob_kld: 0.0096 - dist_relevant_mae: 0.7551 - dist_relevant_mse: 1.9221 - val_loss: 0.2019 - val_prob_loss: 0.0538 - val_dist_loss: 0.7403 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.7400 - val_dist_relevant_mse: 1.7825\n",
      "Epoch 192/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2446 - prob_loss: 0.0806 - dist_loss: 0.8198 - prob_kld: 0.0100 - dist_relevant_mae: 0.8195 - dist_relevant_mse: 2.1710 - val_loss: 0.2036 - val_prob_loss: 0.0533 - val_dist_loss: 0.7519 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7516 - val_dist_relevant_mse: 1.8303\n",
      "Epoch 193/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2339 - prob_loss: 0.0808 - dist_loss: 0.7652 - prob_kld: 0.0090 - dist_relevant_mae: 0.7649 - dist_relevant_mse: 1.9993 - val_loss: 0.2066 - val_prob_loss: 0.0535 - val_dist_loss: 0.7657 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7655 - val_dist_relevant_mse: 1.8720\n",
      "Epoch 194/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2305 - prob_loss: 0.0799 - dist_loss: 0.7529 - prob_kld: 0.0089 - dist_relevant_mae: 0.7526 - dist_relevant_mse: 1.9321 - val_loss: 0.1995 - val_prob_loss: 0.0534 - val_dist_loss: 0.7303 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7300 - val_dist_relevant_mse: 1.7581\n",
      "Epoch 195/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2307 - prob_loss: 0.0766 - dist_loss: 0.7704 - prob_kld: 0.0086 - dist_relevant_mae: 0.7701 - dist_relevant_mse: 1.9618 - val_loss: 0.2216 - val_prob_loss: 0.0542 - val_dist_loss: 0.8371 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.8369 - val_dist_relevant_mse: 2.1254\n",
      "Epoch 196/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2474 - prob_loss: 0.0851 - dist_loss: 0.8115 - prob_kld: 0.0118 - dist_relevant_mae: 0.8112 - dist_relevant_mse: 2.1399 - val_loss: 0.2027 - val_prob_loss: 0.0548 - val_dist_loss: 0.7396 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 0.7393 - val_dist_relevant_mse: 1.8171\n",
      "Epoch 197/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2400 - prob_loss: 0.0785 - dist_loss: 0.8075 - prob_kld: 0.0096 - dist_relevant_mae: 0.8072 - dist_relevant_mse: 2.0870 - val_loss: 0.2130 - val_prob_loss: 0.0534 - val_dist_loss: 0.7979 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7976 - val_dist_relevant_mse: 1.8555\n",
      "Epoch 198/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2345 - prob_loss: 0.0822 - dist_loss: 0.7615 - prob_kld: 0.0093 - dist_relevant_mae: 0.7612 - dist_relevant_mse: 1.9419 - val_loss: 0.2054 - val_prob_loss: 0.0534 - val_dist_loss: 0.7599 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7596 - val_dist_relevant_mse: 1.7867\n",
      "Epoch 199/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2326 - prob_loss: 0.0801 - dist_loss: 0.7621 - prob_kld: 0.0090 - dist_relevant_mae: 0.7618 - dist_relevant_mse: 1.9259 - val_loss: 0.1972 - val_prob_loss: 0.0533 - val_dist_loss: 0.7196 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7193 - val_dist_relevant_mse: 1.7229\n",
      "Epoch 200/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2283 - prob_loss: 0.0794 - dist_loss: 0.7448 - prob_kld: 0.0088 - dist_relevant_mae: 0.7445 - dist_relevant_mse: 1.9375 - val_loss: 0.1958 - val_prob_loss: 0.0532 - val_dist_loss: 0.7131 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7128 - val_dist_relevant_mse: 1.7036\n",
      "Epoch 201/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2407 - prob_loss: 0.0841 - dist_loss: 0.7834 - prob_kld: 0.0101 - dist_relevant_mae: 0.7831 - dist_relevant_mse: 1.9883 - val_loss: 0.2099 - val_prob_loss: 0.0542 - val_dist_loss: 0.7784 - val_prob_kld: 0.0066 - val_dist_relevant_mae: 0.7781 - val_dist_relevant_mse: 1.8775\n",
      "Epoch 202/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2252 - prob_loss: 0.0743 - dist_loss: 0.7546 - prob_kld: 0.0085 - dist_relevant_mae: 0.7543 - dist_relevant_mse: 1.9567 - val_loss: 0.2162 - val_prob_loss: 0.0561 - val_dist_loss: 0.8003 - val_prob_kld: 0.0086 - val_dist_relevant_mae: 0.8000 - val_dist_relevant_mse: 1.9713\n",
      "Epoch 203/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2345 - prob_loss: 0.0826 - dist_loss: 0.7592 - prob_kld: 0.0099 - dist_relevant_mae: 0.7589 - dist_relevant_mse: 1.9197 - val_loss: 0.2048 - val_prob_loss: 0.0536 - val_dist_loss: 0.7559 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7556 - val_dist_relevant_mse: 1.7849\n",
      "Epoch 204/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2365 - prob_loss: 0.0778 - dist_loss: 0.7934 - prob_kld: 0.0092 - dist_relevant_mae: 0.7932 - dist_relevant_mse: 2.0093 - val_loss: 0.1989 - val_prob_loss: 0.0533 - val_dist_loss: 0.7280 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7277 - val_dist_relevant_mse: 1.7292\n",
      "Epoch 205/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2317 - prob_loss: 0.0807 - dist_loss: 0.7550 - prob_kld: 0.0089 - dist_relevant_mae: 0.7547 - dist_relevant_mse: 1.9048 - val_loss: 0.1997 - val_prob_loss: 0.0534 - val_dist_loss: 0.7313 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7311 - val_dist_relevant_mse: 1.7490\n",
      "Epoch 206/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2296 - prob_loss: 0.0782 - dist_loss: 0.7568 - prob_kld: 0.0088 - dist_relevant_mae: 0.7565 - dist_relevant_mse: 1.9203 - val_loss: 0.1989 - val_prob_loss: 0.0538 - val_dist_loss: 0.7254 - val_prob_kld: 0.0062 - val_dist_relevant_mae: 0.7251 - val_dist_relevant_mse: 1.7205\n",
      "Epoch 207/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2379 - prob_loss: 0.0782 - dist_loss: 0.7982 - prob_kld: 0.0089 - dist_relevant_mae: 0.7979 - dist_relevant_mse: 2.0058 - val_loss: 0.2026 - val_prob_loss: 0.0535 - val_dist_loss: 0.7451 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7448 - val_dist_relevant_mse: 1.8029\n",
      "Epoch 208/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 171s 2s/step - loss: 0.2380 - prob_loss: 0.0834 - dist_loss: 0.7730 - prob_kld: 0.0097 - dist_relevant_mae: 0.7727 - dist_relevant_mse: 2.0449 - val_loss: 0.1958 - val_prob_loss: 0.0531 - val_dist_loss: 0.7138 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7135 - val_dist_relevant_mse: 1.6811\n",
      "Epoch 209/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2323 - prob_loss: 0.0825 - dist_loss: 0.7491 - prob_kld: 0.0094 - dist_relevant_mae: 0.7489 - dist_relevant_mse: 1.8708 - val_loss: 0.2101 - val_prob_loss: 0.0535 - val_dist_loss: 0.7833 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7830 - val_dist_relevant_mse: 1.8700\n",
      "Epoch 210/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2456 - prob_loss: 0.0847 - dist_loss: 0.8046 - prob_kld: 0.0102 - dist_relevant_mae: 0.8043 - dist_relevant_mse: 2.0888 - val_loss: 0.2077 - val_prob_loss: 0.0536 - val_dist_loss: 0.7707 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7704 - val_dist_relevant_mse: 1.8599\n",
      "Epoch 211/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2315 - prob_loss: 0.0788 - dist_loss: 0.7634 - prob_kld: 0.0090 - dist_relevant_mae: 0.7631 - dist_relevant_mse: 1.9621 - val_loss: 0.2011 - val_prob_loss: 0.0536 - val_dist_loss: 0.7379 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7376 - val_dist_relevant_mse: 1.7595\n",
      "Epoch 212/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2255 - prob_loss: 0.0757 - dist_loss: 0.7490 - prob_kld: 0.0086 - dist_relevant_mae: 0.7487 - dist_relevant_mse: 1.8799 - val_loss: 0.2018 - val_prob_loss: 0.0535 - val_dist_loss: 0.7418 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7415 - val_dist_relevant_mse: 1.8018\n",
      "Epoch 213/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2288 - prob_loss: 0.0765 - dist_loss: 0.7616 - prob_kld: 0.0086 - dist_relevant_mae: 0.7613 - dist_relevant_mse: 1.9443 - val_loss: 0.2075 - val_prob_loss: 0.0539 - val_dist_loss: 0.7680 - val_prob_kld: 0.0063 - val_dist_relevant_mae: 0.7677 - val_dist_relevant_mse: 1.8086\n",
      "Epoch 214/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2331 - prob_loss: 0.0808 - dist_loss: 0.7613 - prob_kld: 0.0092 - dist_relevant_mae: 0.7611 - dist_relevant_mse: 1.9548 - val_loss: 0.2155 - val_prob_loss: 0.0541 - val_dist_loss: 0.8074 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.8071 - val_dist_relevant_mse: 1.9602\n",
      "Epoch 215/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2314 - prob_loss: 0.0813 - dist_loss: 0.7507 - prob_kld: 0.0095 - dist_relevant_mae: 0.7504 - dist_relevant_mse: 1.9118 - val_loss: 0.1996 - val_prob_loss: 0.0534 - val_dist_loss: 0.7309 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7307 - val_dist_relevant_mse: 1.7524\n",
      "Epoch 216/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2294 - prob_loss: 0.0752 - dist_loss: 0.7711 - prob_kld: 0.0085 - dist_relevant_mae: 0.7708 - dist_relevant_mse: 1.9519 - val_loss: 0.2036 - val_prob_loss: 0.0536 - val_dist_loss: 0.7501 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7498 - val_dist_relevant_mse: 1.7607\n",
      "Epoch 217/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2306 - prob_loss: 0.0790 - dist_loss: 0.7580 - prob_kld: 0.0089 - dist_relevant_mae: 0.7577 - dist_relevant_mse: 1.9003 - val_loss: 0.2057 - val_prob_loss: 0.0543 - val_dist_loss: 0.7572 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 0.7569 - val_dist_relevant_mse: 1.8587\n",
      "Epoch 218/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2372 - prob_loss: 0.0866 - dist_loss: 0.7528 - prob_kld: 0.0096 - dist_relevant_mae: 0.7525 - dist_relevant_mse: 1.9478 - val_loss: 0.2014 - val_prob_loss: 0.0533 - val_dist_loss: 0.7407 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7404 - val_dist_relevant_mse: 1.7765\n",
      "Epoch 219/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2364 - prob_loss: 0.0827 - dist_loss: 0.7687 - prob_kld: 0.0096 - dist_relevant_mae: 0.7685 - dist_relevant_mse: 1.9438 - val_loss: 0.2034 - val_prob_loss: 0.0541 - val_dist_loss: 0.7464 - val_prob_kld: 0.0065 - val_dist_relevant_mae: 0.7461 - val_dist_relevant_mse: 1.8208\n",
      "Epoch 220/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2277 - prob_loss: 0.0805 - dist_loss: 0.7361 - prob_kld: 0.0088 - dist_relevant_mae: 0.7358 - dist_relevant_mse: 1.8264 - val_loss: 0.2025 - val_prob_loss: 0.0533 - val_dist_loss: 0.7464 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7461 - val_dist_relevant_mse: 1.7930\n",
      "Epoch 221/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2365 - prob_loss: 0.0858 - dist_loss: 0.7535 - prob_kld: 0.0097 - dist_relevant_mae: 0.7532 - dist_relevant_mse: 1.9056 - val_loss: 0.2072 - val_prob_loss: 0.0547 - val_dist_loss: 0.7624 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 0.7621 - val_dist_relevant_mse: 1.8117\n",
      "Epoch 222/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2231 - prob_loss: 0.0742 - dist_loss: 0.7445 - prob_kld: 0.0084 - dist_relevant_mae: 0.7443 - dist_relevant_mse: 1.8489 - val_loss: 0.1982 - val_prob_loss: 0.0532 - val_dist_loss: 0.7247 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7244 - val_dist_relevant_mse: 1.7322\n",
      "Epoch 223/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2254 - prob_loss: 0.0769 - dist_loss: 0.7429 - prob_kld: 0.0084 - dist_relevant_mae: 0.7426 - dist_relevant_mse: 1.8814 - val_loss: 0.1974 - val_prob_loss: 0.0532 - val_dist_loss: 0.7213 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7211 - val_dist_relevant_mse: 1.7329\n",
      "Epoch 224/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2385 - prob_loss: 0.0789 - dist_loss: 0.7981 - prob_kld: 0.0095 - dist_relevant_mae: 0.7978 - dist_relevant_mse: 2.0575 - val_loss: 0.2393 - val_prob_loss: 0.0563 - val_dist_loss: 0.9150 - val_prob_kld: 0.0087 - val_dist_relevant_mae: 0.9147 - val_dist_relevant_mse: 2.3613\n",
      "Epoch 225/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2363 - prob_loss: 0.0772 - dist_loss: 0.7955 - prob_kld: 0.0098 - dist_relevant_mae: 0.7952 - dist_relevant_mse: 2.0523 - val_loss: 0.2033 - val_prob_loss: 0.0532 - val_dist_loss: 0.7507 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7505 - val_dist_relevant_mse: 1.8261\n",
      "Epoch 226/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2331 - prob_loss: 0.0815 - dist_loss: 0.7579 - prob_kld: 0.0091 - dist_relevant_mae: 0.7576 - dist_relevant_mse: 1.9226 - val_loss: 0.1985 - val_prob_loss: 0.0535 - val_dist_loss: 0.7251 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7248 - val_dist_relevant_mse: 1.7375\n",
      "Epoch 227/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2223 - prob_loss: 0.0752 - dist_loss: 0.7356 - prob_kld: 0.0085 - dist_relevant_mae: 0.7353 - dist_relevant_mse: 1.8594 - val_loss: 0.2047 - val_prob_loss: 0.0535 - val_dist_loss: 0.7560 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7557 - val_dist_relevant_mse: 1.7877\n",
      "Epoch 228/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2255 - prob_loss: 0.0755 - dist_loss: 0.7499 - prob_kld: 0.0090 - dist_relevant_mae: 0.7497 - dist_relevant_mse: 1.8838 - val_loss: 0.2112 - val_prob_loss: 0.0536 - val_dist_loss: 0.7876 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7874 - val_dist_relevant_mse: 1.9250\n",
      "Epoch 229/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2312 - prob_loss: 0.0777 - dist_loss: 0.7676 - prob_kld: 0.0091 - dist_relevant_mae: 0.7673 - dist_relevant_mse: 1.9608 - val_loss: 0.1994 - val_prob_loss: 0.0534 - val_dist_loss: 0.7297 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7294 - val_dist_relevant_mse: 1.7346\n",
      "Epoch 230/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2332 - prob_loss: 0.0808 - dist_loss: 0.7621 - prob_kld: 0.0091 - dist_relevant_mae: 0.7618 - dist_relevant_mse: 1.9490 - val_loss: 0.1968 - val_prob_loss: 0.0532 - val_dist_loss: 0.7179 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7176 - val_dist_relevant_mse: 1.6884\n",
      "Epoch 231/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2299 - prob_loss: 0.0811 - dist_loss: 0.7440 - prob_kld: 0.0094 - dist_relevant_mae: 0.7437 - dist_relevant_mse: 1.8923 - val_loss: 0.2004 - val_prob_loss: 0.0532 - val_dist_loss: 0.7360 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7357 - val_dist_relevant_mse: 1.7294\n",
      "Epoch 232/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2305 - prob_loss: 0.0822 - dist_loss: 0.7419 - prob_kld: 0.0093 - dist_relevant_mae: 0.7416 - dist_relevant_mse: 1.8432 - val_loss: 0.1964 - val_prob_loss: 0.0532 - val_dist_loss: 0.7160 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7157 - val_dist_relevant_mse: 1.6924\n",
      "Epoch 233/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2372 - prob_loss: 0.0812 - dist_loss: 0.7801 - prob_kld: 0.0090 - dist_relevant_mae: 0.7798 - dist_relevant_mse: 1.9618 - val_loss: 0.2013 - val_prob_loss: 0.0532 - val_dist_loss: 0.7404 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7401 - val_dist_relevant_mse: 1.7388\n",
      "Epoch 234/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2277 - prob_loss: 0.0789 - dist_loss: 0.7436 - prob_kld: 0.0088 - dist_relevant_mae: 0.7433 - dist_relevant_mse: 1.8479 - val_loss: 0.1986 - val_prob_loss: 0.0532 - val_dist_loss: 0.7268 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7265 - val_dist_relevant_mse: 1.7292\n",
      "Epoch 235/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2273 - prob_loss: 0.0776 - dist_loss: 0.7481 - prob_kld: 0.0087 - dist_relevant_mae: 0.7478 - dist_relevant_mse: 1.8516 - val_loss: 0.1993 - val_prob_loss: 0.0532 - val_dist_loss: 0.7305 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7302 - val_dist_relevant_mse: 1.7095\n",
      "Epoch 236/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2273 - prob_loss: 0.0783 - dist_loss: 0.7450 - prob_kld: 0.0083 - dist_relevant_mae: 0.7448 - dist_relevant_mse: 1.8432 - val_loss: 0.2062 - val_prob_loss: 0.0534 - val_dist_loss: 0.7642 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7640 - val_dist_relevant_mse: 1.8109\n",
      "Epoch 237/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2275 - prob_loss: 0.0807 - dist_loss: 0.7339 - prob_kld: 0.0087 - dist_relevant_mae: 0.7336 - dist_relevant_mse: 1.8527 - val_loss: 0.1994 - val_prob_loss: 0.0531 - val_dist_loss: 0.7317 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7314 - val_dist_relevant_mse: 1.6911\n",
      "Epoch 238/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2210 - prob_loss: 0.0772 - dist_loss: 0.7194 - prob_kld: 0.0085 - dist_relevant_mae: 0.7192 - dist_relevant_mse: 1.7721 - val_loss: 0.2051 - val_prob_loss: 0.0535 - val_dist_loss: 0.7582 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7580 - val_dist_relevant_mse: 1.7602\n",
      "Epoch 239/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2255 - prob_loss: 0.0825 - dist_loss: 0.7150 - prob_kld: 0.0087 - dist_relevant_mae: 0.7147 - dist_relevant_mse: 1.7869 - val_loss: 0.1970 - val_prob_loss: 0.0531 - val_dist_loss: 0.7194 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7191 - val_dist_relevant_mse: 1.7049\n",
      "Epoch 240/400\n",
      " 99/100 [============================>.] - ETA: 1s - loss: 0.2244 - prob_loss: 0.0785 - dist_loss: 0.7294 - prob_kld: 0.0083 - dist_relevant_mae: 0.7292 - dist_relevant_mse: 1.8338\n",
      "Epoch 00240: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2244 - prob_loss: 0.0784 - dist_loss: 0.7298 - prob_kld: 0.0083 - dist_relevant_mae: 0.7295 - dist_relevant_mse: 1.8365 - val_loss: 0.1991 - val_prob_loss: 0.0532 - val_dist_loss: 0.7295 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7292 - val_dist_relevant_mse: 1.6936\n",
      "Epoch 241/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2267 - prob_loss: 0.0837 - dist_loss: 0.7152 - prob_kld: 0.0086 - dist_relevant_mae: 0.7149 - dist_relevant_mse: 1.7320 - val_loss: 0.1949 - val_prob_loss: 0.0531 - val_dist_loss: 0.7088 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7085 - val_dist_relevant_mse: 1.6486\n",
      "Epoch 242/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2143 - prob_loss: 0.0712 - dist_loss: 0.7153 - prob_kld: 0.0074 - dist_relevant_mae: 0.7150 - dist_relevant_mse: 1.7681 - val_loss: 0.1958 - val_prob_loss: 0.0533 - val_dist_loss: 0.7124 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7121 - val_dist_relevant_mse: 1.6674\n",
      "Epoch 243/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2157 - prob_loss: 0.0731 - dist_loss: 0.7131 - prob_kld: 0.0077 - dist_relevant_mae: 0.7128 - dist_relevant_mse: 1.7792 - val_loss: 0.1928 - val_prob_loss: 0.0530 - val_dist_loss: 0.6987 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6984 - val_dist_relevant_mse: 1.6182\n",
      "Epoch 244/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2245 - prob_loss: 0.0797 - dist_loss: 0.7237 - prob_kld: 0.0083 - dist_relevant_mae: 0.7234 - dist_relevant_mse: 1.7847 - val_loss: 0.2036 - val_prob_loss: 0.0532 - val_dist_loss: 0.7520 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7518 - val_dist_relevant_mse: 1.7525\n",
      "Epoch 245/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2249 - prob_loss: 0.0828 - dist_loss: 0.7105 - prob_kld: 0.0087 - dist_relevant_mae: 0.7102 - dist_relevant_mse: 1.7412 - val_loss: 0.1969 - val_prob_loss: 0.0533 - val_dist_loss: 0.7182 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7179 - val_dist_relevant_mse: 1.6854\n",
      "Epoch 246/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2187 - prob_loss: 0.0785 - dist_loss: 0.7009 - prob_kld: 0.0082 - dist_relevant_mae: 0.7006 - dist_relevant_mse: 1.7314 - val_loss: 0.1923 - val_prob_loss: 0.0530 - val_dist_loss: 0.6963 - val_prob_kld: 0.0054 - val_dist_relevant_mae: 0.6961 - val_dist_relevant_mse: 1.6160\n",
      "Epoch 247/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2176 - prob_loss: 0.0786 - dist_loss: 0.6948 - prob_kld: 0.0079 - dist_relevant_mae: 0.6945 - dist_relevant_mse: 1.7405 - val_loss: 0.1931 - val_prob_loss: 0.0530 - val_dist_loss: 0.7006 - val_prob_kld: 0.0054 - val_dist_relevant_mae: 0.7004 - val_dist_relevant_mse: 1.6394\n",
      "Epoch 248/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2195 - prob_loss: 0.0797 - dist_loss: 0.6990 - prob_kld: 0.0081 - dist_relevant_mae: 0.6987 - dist_relevant_mse: 1.7318 - val_loss: 0.1955 - val_prob_loss: 0.0532 - val_dist_loss: 0.7115 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7113 - val_dist_relevant_mse: 1.6409\n",
      "Epoch 249/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2152 - prob_loss: 0.0771 - dist_loss: 0.6903 - prob_kld: 0.0078 - dist_relevant_mae: 0.6900 - dist_relevant_mse: 1.6941 - val_loss: 0.1966 - val_prob_loss: 0.0532 - val_dist_loss: 0.7168 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7165 - val_dist_relevant_mse: 1.6647\n",
      "Epoch 250/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2177 - prob_loss: 0.0753 - dist_loss: 0.7121 - prob_kld: 0.0078 - dist_relevant_mae: 0.7118 - dist_relevant_mse: 1.7207 - val_loss: 0.1963 - val_prob_loss: 0.0532 - val_dist_loss: 0.7157 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7155 - val_dist_relevant_mse: 1.6674\n",
      "Epoch 251/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2215 - prob_loss: 0.0812 - dist_loss: 0.7017 - prob_kld: 0.0083 - dist_relevant_mae: 0.7015 - dist_relevant_mse: 1.6981 - val_loss: 0.1948 - val_prob_loss: 0.0533 - val_dist_loss: 0.7078 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7076 - val_dist_relevant_mse: 1.6601\n",
      "Epoch 252/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2220 - prob_loss: 0.0812 - dist_loss: 0.7043 - prob_kld: 0.0082 - dist_relevant_mae: 0.7040 - dist_relevant_mse: 1.7373 - val_loss: 0.1964 - val_prob_loss: 0.0532 - val_dist_loss: 0.7160 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7158 - val_dist_relevant_mse: 1.6960\n",
      "Epoch 253/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2226 - prob_loss: 0.0798 - dist_loss: 0.7139 - prob_kld: 0.0082 - dist_relevant_mae: 0.7136 - dist_relevant_mse: 1.7136 - val_loss: 0.1996 - val_prob_loss: 0.0534 - val_dist_loss: 0.7310 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7308 - val_dist_relevant_mse: 1.7338\n",
      "Epoch 254/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2221 - prob_loss: 0.0768 - dist_loss: 0.7268 - prob_kld: 0.0081 - dist_relevant_mae: 0.7266 - dist_relevant_mse: 1.7973 - val_loss: 0.1978 - val_prob_loss: 0.0533 - val_dist_loss: 0.7222 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7220 - val_dist_relevant_mse: 1.6723\n",
      "Epoch 255/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2245 - prob_loss: 0.0829 - dist_loss: 0.7081 - prob_kld: 0.0086 - dist_relevant_mae: 0.7079 - dist_relevant_mse: 1.7386 - val_loss: 0.1948 - val_prob_loss: 0.0533 - val_dist_loss: 0.7075 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7072 - val_dist_relevant_mse: 1.6587\n",
      "Epoch 256/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2166 - prob_loss: 0.0748 - dist_loss: 0.7089 - prob_kld: 0.0079 - dist_relevant_mae: 0.7086 - dist_relevant_mse: 1.7409 - val_loss: 0.1952 - val_prob_loss: 0.0533 - val_dist_loss: 0.7095 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7092 - val_dist_relevant_mse: 1.6679\n",
      "Epoch 257/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2197 - prob_loss: 0.0787 - dist_loss: 0.7048 - prob_kld: 0.0078 - dist_relevant_mae: 0.7046 - dist_relevant_mse: 1.7825 - val_loss: 0.1951 - val_prob_loss: 0.0533 - val_dist_loss: 0.7086 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7084 - val_dist_relevant_mse: 1.6585\n",
      "Epoch 258/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2096 - prob_loss: 0.0699 - dist_loss: 0.6985 - prob_kld: 0.0072 - dist_relevant_mae: 0.6982 - dist_relevant_mse: 1.7042 - val_loss: 0.1947 - val_prob_loss: 0.0532 - val_dist_loss: 0.7072 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7069 - val_dist_relevant_mse: 1.6371\n",
      "Epoch 259/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2091 - prob_loss: 0.0715 - dist_loss: 0.6883 - prob_kld: 0.0073 - dist_relevant_mae: 0.6880 - dist_relevant_mse: 1.6352 - val_loss: 0.2013 - val_prob_loss: 0.0535 - val_dist_loss: 0.7389 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7386 - val_dist_relevant_mse: 1.7515\n",
      "Epoch 260/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2181 - prob_loss: 0.0766 - dist_loss: 0.7073 - prob_kld: 0.0080 - dist_relevant_mae: 0.7070 - dist_relevant_mse: 1.7136 - val_loss: 0.1974 - val_prob_loss: 0.0533 - val_dist_loss: 0.7207 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7204 - val_dist_relevant_mse: 1.6914\n",
      "Epoch 261/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2228 - prob_loss: 0.0815 - dist_loss: 0.7064 - prob_kld: 0.0083 - dist_relevant_mae: 0.7062 - dist_relevant_mse: 1.7178 - val_loss: 0.1967 - val_prob_loss: 0.0535 - val_dist_loss: 0.7162 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7159 - val_dist_relevant_mse: 1.6744\n",
      "Epoch 262/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2172 - prob_loss: 0.0763 - dist_loss: 0.7045 - prob_kld: 0.0078 - dist_relevant_mae: 0.7042 - dist_relevant_mse: 1.7214 - val_loss: 0.1950 - val_prob_loss: 0.0532 - val_dist_loss: 0.7087 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7085 - val_dist_relevant_mse: 1.6527\n",
      "Epoch 263/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2128 - prob_loss: 0.0724 - dist_loss: 0.7015 - prob_kld: 0.0075 - dist_relevant_mae: 0.7013 - dist_relevant_mse: 1.6753 - val_loss: 0.1929 - val_prob_loss: 0.0530 - val_dist_loss: 0.6991 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6989 - val_dist_relevant_mse: 1.6166\n",
      "Epoch 264/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2151 - prob_loss: 0.0744 - dist_loss: 0.7038 - prob_kld: 0.0076 - dist_relevant_mae: 0.7035 - dist_relevant_mse: 1.7234 - val_loss: 0.1960 - val_prob_loss: 0.0532 - val_dist_loss: 0.7138 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7136 - val_dist_relevant_mse: 1.6815\n",
      "Epoch 265/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2200 - prob_loss: 0.0795 - dist_loss: 0.7023 - prob_kld: 0.0080 - dist_relevant_mae: 0.7021 - dist_relevant_mse: 1.6999 - val_loss: 0.1933 - val_prob_loss: 0.0530 - val_dist_loss: 0.7014 - val_prob_kld: 0.0054 - val_dist_relevant_mae: 0.7012 - val_dist_relevant_mse: 1.6318\n",
      "Epoch 266/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2183 - prob_loss: 0.0765 - dist_loss: 0.7095 - prob_kld: 0.0078 - dist_relevant_mae: 0.7092 - dist_relevant_mse: 1.7316 - val_loss: 0.1943 - val_prob_loss: 0.0531 - val_dist_loss: 0.7059 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7056 - val_dist_relevant_mse: 1.6219\n",
      "Epoch 267/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2155 - prob_loss: 0.0730 - dist_loss: 0.7128 - prob_kld: 0.0074 - dist_relevant_mae: 0.7126 - dist_relevant_mse: 1.7521 - val_loss: 0.1984 - val_prob_loss: 0.0536 - val_dist_loss: 0.7237 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7234 - val_dist_relevant_mse: 1.6844\n",
      "Epoch 268/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2161 - prob_loss: 0.0801 - dist_loss: 0.6800 - prob_kld: 0.0080 - dist_relevant_mae: 0.6798 - dist_relevant_mse: 1.6244 - val_loss: 0.1947 - val_prob_loss: 0.0533 - val_dist_loss: 0.7069 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7067 - val_dist_relevant_mse: 1.6367\n",
      "Epoch 269/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2151 - prob_loss: 0.0748 - dist_loss: 0.7018 - prob_kld: 0.0075 - dist_relevant_mae: 0.7016 - dist_relevant_mse: 1.6880 - val_loss: 0.1971 - val_prob_loss: 0.0532 - val_dist_loss: 0.7192 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7189 - val_dist_relevant_mse: 1.6705\n",
      "Epoch 270/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2191 - prob_loss: 0.0773 - dist_loss: 0.7088 - prob_kld: 0.0079 - dist_relevant_mae: 0.7085 - dist_relevant_mse: 1.6932 - val_loss: 0.1962 - val_prob_loss: 0.0533 - val_dist_loss: 0.7145 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7143 - val_dist_relevant_mse: 1.7029\n",
      "Epoch 271/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2145 - prob_loss: 0.0752 - dist_loss: 0.6964 - prob_kld: 0.0075 - dist_relevant_mae: 0.6961 - dist_relevant_mse: 1.6998 - val_loss: 0.1953 - val_prob_loss: 0.0534 - val_dist_loss: 0.7096 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7093 - val_dist_relevant_mse: 1.6676\n",
      "Epoch 272/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2156 - prob_loss: 0.0737 - dist_loss: 0.7098 - prob_kld: 0.0076 - dist_relevant_mae: 0.7095 - dist_relevant_mse: 1.6979 - val_loss: 0.1970 - val_prob_loss: 0.0534 - val_dist_loss: 0.7183 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7181 - val_dist_relevant_mse: 1.7150\n",
      "Epoch 273/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2189 - prob_loss: 0.0775 - dist_loss: 0.7068 - prob_kld: 0.0080 - dist_relevant_mae: 0.7065 - dist_relevant_mse: 1.6825 - val_loss: 0.1985 - val_prob_loss: 0.0536 - val_dist_loss: 0.7245 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7242 - val_dist_relevant_mse: 1.7108\n",
      "Epoch 274/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2241 - prob_loss: 0.0811 - dist_loss: 0.7152 - prob_kld: 0.0084 - dist_relevant_mae: 0.7150 - dist_relevant_mse: 1.7261 - val_loss: 0.2005 - val_prob_loss: 0.0536 - val_dist_loss: 0.7346 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7343 - val_dist_relevant_mse: 1.7553\n",
      "Epoch 275/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2192 - prob_loss: 0.0813 - dist_loss: 0.6892 - prob_kld: 0.0083 - dist_relevant_mae: 0.6889 - dist_relevant_mse: 1.6791 - val_loss: 0.1949 - val_prob_loss: 0.0533 - val_dist_loss: 0.7078 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7075 - val_dist_relevant_mse: 1.6607\n",
      "Epoch 276/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2169 - prob_loss: 0.0779 - dist_loss: 0.6953 - prob_kld: 0.0077 - dist_relevant_mae: 0.6950 - dist_relevant_mse: 1.6829 - val_loss: 0.1976 - val_prob_loss: 0.0534 - val_dist_loss: 0.7214 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7212 - val_dist_relevant_mse: 1.6943\n",
      "Epoch 277/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2193 - prob_loss: 0.0810 - dist_loss: 0.6917 - prob_kld: 0.0081 - dist_relevant_mae: 0.6915 - dist_relevant_mse: 1.6836 - val_loss: 0.1949 - val_prob_loss: 0.0532 - val_dist_loss: 0.7083 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7081 - val_dist_relevant_mse: 1.6391\n",
      "Epoch 278/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2183 - prob_loss: 0.0794 - dist_loss: 0.6944 - prob_kld: 0.0077 - dist_relevant_mae: 0.6941 - dist_relevant_mse: 1.6869 - val_loss: 0.1940 - val_prob_loss: 0.0531 - val_dist_loss: 0.7045 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7043 - val_dist_relevant_mse: 1.6373\n",
      "Epoch 279/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2136 - prob_loss: 0.0754 - dist_loss: 0.6911 - prob_kld: 0.0074 - dist_relevant_mae: 0.6908 - dist_relevant_mse: 1.6861 - val_loss: 0.1958 - val_prob_loss: 0.0531 - val_dist_loss: 0.7136 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7134 - val_dist_relevant_mse: 1.6620\n",
      "Epoch 280/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2181 - prob_loss: 0.0808 - dist_loss: 0.6868 - prob_kld: 0.0079 - dist_relevant_mae: 0.6866 - dist_relevant_mse: 1.6631 - val_loss: 0.1926 - val_prob_loss: 0.0531 - val_dist_loss: 0.6974 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6972 - val_dist_relevant_mse: 1.6237\n",
      "Epoch 281/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2173 - prob_loss: 0.0774 - dist_loss: 0.6994 - prob_kld: 0.0077 - dist_relevant_mae: 0.6991 - dist_relevant_mse: 1.7342 - val_loss: 0.1935 - val_prob_loss: 0.0532 - val_dist_loss: 0.7014 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7012 - val_dist_relevant_mse: 1.6326\n",
      "Epoch 282/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2217 - prob_loss: 0.0847 - dist_loss: 0.6849 - prob_kld: 0.0082 - dist_relevant_mae: 0.6847 - dist_relevant_mse: 1.6558 - val_loss: 0.1936 - val_prob_loss: 0.0531 - val_dist_loss: 0.7023 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.7020 - val_dist_relevant_mse: 1.6197\n",
      "Epoch 283/400\n",
      "100/100 [==============================] - 170s 2s/step - loss: 0.2141 - prob_loss: 0.0755 - dist_loss: 0.6932 - prob_kld: 0.0075 - dist_relevant_mae: 0.6929 - dist_relevant_mse: 1.6426 - val_loss: 0.1995 - val_prob_loss: 0.0533 - val_dist_loss: 0.7315 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7312 - val_dist_relevant_mse: 1.6648\n",
      "Epoch 284/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2177 - prob_loss: 0.0778 - dist_loss: 0.6992 - prob_kld: 0.0077 - dist_relevant_mae: 0.6989 - dist_relevant_mse: 1.7210 - val_loss: 0.1960 - val_prob_loss: 0.0533 - val_dist_loss: 0.7133 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7131 - val_dist_relevant_mse: 1.6215\n",
      "Epoch 285/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2182 - prob_loss: 0.0766 - dist_loss: 0.7079 - prob_kld: 0.0079 - dist_relevant_mae: 0.7076 - dist_relevant_mse: 1.6983 - val_loss: 0.1977 - val_prob_loss: 0.0534 - val_dist_loss: 0.7217 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7215 - val_dist_relevant_mse: 1.6326\n",
      "Epoch 286/400\n",
      " 99/100 [============================>.] - ETA: 1s - loss: 0.2242 - prob_loss: 0.0822 - dist_loss: 0.7099 - prob_kld: 0.0083 - dist_relevant_mae: 0.7096 - dist_relevant_mse: 1.7029\n",
      "Epoch 00286: ReduceLROnPlateau reducing learning rate to 7.500000356230885e-05.\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2235 - prob_loss: 0.0815 - dist_loss: 0.7097 - prob_kld: 0.0083 - dist_relevant_mae: 0.7094 - dist_relevant_mse: 1.6998 - val_loss: 0.1974 - val_prob_loss: 0.0537 - val_dist_loss: 0.7185 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7183 - val_dist_relevant_mse: 1.6875\n",
      "Epoch 287/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2193 - prob_loss: 0.0810 - dist_loss: 0.6912 - prob_kld: 0.0079 - dist_relevant_mae: 0.6910 - dist_relevant_mse: 1.6913 - val_loss: 0.1925 - val_prob_loss: 0.0531 - val_dist_loss: 0.6969 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6966 - val_dist_relevant_mse: 1.6304\n",
      "Epoch 288/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2167 - prob_loss: 0.0814 - dist_loss: 0.6767 - prob_kld: 0.0079 - dist_relevant_mae: 0.6765 - dist_relevant_mse: 1.5754 - val_loss: 0.1927 - val_prob_loss: 0.0532 - val_dist_loss: 0.6976 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.6973 - val_dist_relevant_mse: 1.6282\n",
      "Epoch 289/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2093 - prob_loss: 0.0743 - dist_loss: 0.6752 - prob_kld: 0.0072 - dist_relevant_mae: 0.6750 - dist_relevant_mse: 1.6271 - val_loss: 0.1936 - val_prob_loss: 0.0532 - val_dist_loss: 0.7021 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7019 - val_dist_relevant_mse: 1.6405\n",
      "Epoch 290/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2067 - prob_loss: 0.0714 - dist_loss: 0.6769 - prob_kld: 0.0068 - dist_relevant_mae: 0.6767 - dist_relevant_mse: 1.6188 - val_loss: 0.1941 - val_prob_loss: 0.0533 - val_dist_loss: 0.7039 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7037 - val_dist_relevant_mse: 1.6290\n",
      "Epoch 291/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2142 - prob_loss: 0.0782 - dist_loss: 0.6801 - prob_kld: 0.0074 - dist_relevant_mae: 0.6799 - dist_relevant_mse: 1.6492 - val_loss: 0.1933 - val_prob_loss: 0.0533 - val_dist_loss: 0.7001 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6999 - val_dist_relevant_mse: 1.6335\n",
      "Epoch 292/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2134 - prob_loss: 0.0778 - dist_loss: 0.6780 - prob_kld: 0.0074 - dist_relevant_mae: 0.6778 - dist_relevant_mse: 1.6171 - val_loss: 0.1928 - val_prob_loss: 0.0533 - val_dist_loss: 0.6974 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6971 - val_dist_relevant_mse: 1.6168\n",
      "Epoch 293/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2167 - prob_loss: 0.0814 - dist_loss: 0.6768 - prob_kld: 0.0079 - dist_relevant_mae: 0.6766 - dist_relevant_mse: 1.6366 - val_loss: 0.1930 - val_prob_loss: 0.0532 - val_dist_loss: 0.6990 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.6987 - val_dist_relevant_mse: 1.6147\n",
      "Epoch 294/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2179 - prob_loss: 0.0816 - dist_loss: 0.6816 - prob_kld: 0.0077 - dist_relevant_mae: 0.6813 - dist_relevant_mse: 1.6398 - val_loss: 0.1951 - val_prob_loss: 0.0535 - val_dist_loss: 0.7079 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7077 - val_dist_relevant_mse: 1.6653\n",
      "Epoch 295/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2153 - prob_loss: 0.0798 - dist_loss: 0.6772 - prob_kld: 0.0076 - dist_relevant_mae: 0.6770 - dist_relevant_mse: 1.6172 - val_loss: 0.1929 - val_prob_loss: 0.0533 - val_dist_loss: 0.6982 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6980 - val_dist_relevant_mse: 1.6294\n",
      "Epoch 296/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2166 - prob_loss: 0.0814 - dist_loss: 0.6761 - prob_kld: 0.0078 - dist_relevant_mae: 0.6759 - dist_relevant_mse: 1.6248 - val_loss: 0.1940 - val_prob_loss: 0.0533 - val_dist_loss: 0.7034 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7032 - val_dist_relevant_mse: 1.6342\n",
      "Epoch 297/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2095 - prob_loss: 0.0737 - dist_loss: 0.6790 - prob_kld: 0.0069 - dist_relevant_mae: 0.6787 - dist_relevant_mse: 1.6238 - val_loss: 0.1933 - val_prob_loss: 0.0533 - val_dist_loss: 0.7003 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7000 - val_dist_relevant_mse: 1.6187\n",
      "Epoch 298/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2101 - prob_loss: 0.0733 - dist_loss: 0.6837 - prob_kld: 0.0071 - dist_relevant_mae: 0.6835 - dist_relevant_mse: 1.6055 - val_loss: 0.1954 - val_prob_loss: 0.0533 - val_dist_loss: 0.7102 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7100 - val_dist_relevant_mse: 1.6610\n",
      "Epoch 299/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2091 - prob_loss: 0.0791 - dist_loss: 0.6502 - prob_kld: 0.0075 - dist_relevant_mae: 0.6500 - dist_relevant_mse: 1.5553 - val_loss: 0.1927 - val_prob_loss: 0.0533 - val_dist_loss: 0.6973 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6971 - val_dist_relevant_mse: 1.6182\n",
      "Epoch 300/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2127 - prob_loss: 0.0790 - dist_loss: 0.6689 - prob_kld: 0.0074 - dist_relevant_mae: 0.6686 - dist_relevant_mse: 1.6241 - val_loss: 0.1937 - val_prob_loss: 0.0535 - val_dist_loss: 0.7012 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7010 - val_dist_relevant_mse: 1.6268\n",
      "Epoch 301/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2117 - prob_loss: 0.0765 - dist_loss: 0.6758 - prob_kld: 0.0073 - dist_relevant_mae: 0.6756 - dist_relevant_mse: 1.5818 - val_loss: 0.1943 - val_prob_loss: 0.0534 - val_dist_loss: 0.7044 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7042 - val_dist_relevant_mse: 1.6431\n",
      "Epoch 302/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2145 - prob_loss: 0.0735 - dist_loss: 0.7053 - prob_kld: 0.0071 - dist_relevant_mae: 0.7051 - dist_relevant_mse: 1.7045 - val_loss: 0.1947 - val_prob_loss: 0.0533 - val_dist_loss: 0.7066 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7063 - val_dist_relevant_mse: 1.6513\n",
      "Epoch 303/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2179 - prob_loss: 0.0811 - dist_loss: 0.6841 - prob_kld: 0.0078 - dist_relevant_mae: 0.6839 - dist_relevant_mse: 1.6801 - val_loss: 0.1920 - val_prob_loss: 0.0533 - val_dist_loss: 0.6935 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6933 - val_dist_relevant_mse: 1.6205\n",
      "Epoch 304/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2138 - prob_loss: 0.0782 - dist_loss: 0.6775 - prob_kld: 0.0074 - dist_relevant_mae: 0.6773 - dist_relevant_mse: 1.6545 - val_loss: 0.1945 - val_prob_loss: 0.0533 - val_dist_loss: 0.7063 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7061 - val_dist_relevant_mse: 1.6544\n",
      "Epoch 305/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2132 - prob_loss: 0.0767 - dist_loss: 0.6823 - prob_kld: 0.0074 - dist_relevant_mae: 0.6821 - dist_relevant_mse: 1.6542 - val_loss: 0.1954 - val_prob_loss: 0.0536 - val_dist_loss: 0.7088 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7086 - val_dist_relevant_mse: 1.6595\n",
      "Epoch 306/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2191 - prob_loss: 0.0782 - dist_loss: 0.7045 - prob_kld: 0.0076 - dist_relevant_mae: 0.7043 - dist_relevant_mse: 1.7404 - val_loss: 0.1936 - val_prob_loss: 0.0533 - val_dist_loss: 0.7019 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7017 - val_dist_relevant_mse: 1.6412\n",
      "Epoch 307/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2166 - prob_loss: 0.0786 - dist_loss: 0.6901 - prob_kld: 0.0075 - dist_relevant_mae: 0.6899 - dist_relevant_mse: 1.6610 - val_loss: 0.1941 - val_prob_loss: 0.0532 - val_dist_loss: 0.7043 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7041 - val_dist_relevant_mse: 1.6470\n",
      "Epoch 308/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2113 - prob_loss: 0.0779 - dist_loss: 0.6674 - prob_kld: 0.0074 - dist_relevant_mae: 0.6672 - dist_relevant_mse: 1.6106 - val_loss: 0.1942 - val_prob_loss: 0.0534 - val_dist_loss: 0.7042 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7039 - val_dist_relevant_mse: 1.6530\n",
      "Epoch 309/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2072 - prob_loss: 0.0732 - dist_loss: 0.6699 - prob_kld: 0.0071 - dist_relevant_mae: 0.6697 - dist_relevant_mse: 1.5625 - val_loss: 0.1917 - val_prob_loss: 0.0532 - val_dist_loss: 0.6923 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6921 - val_dist_relevant_mse: 1.6021\n",
      "Epoch 310/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2154 - prob_loss: 0.0800 - dist_loss: 0.6774 - prob_kld: 0.0077 - dist_relevant_mae: 0.6772 - dist_relevant_mse: 1.6084 - val_loss: 0.1935 - val_prob_loss: 0.0533 - val_dist_loss: 0.7010 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7007 - val_dist_relevant_mse: 1.6354\n",
      "Epoch 311/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2116 - prob_loss: 0.0749 - dist_loss: 0.6832 - prob_kld: 0.0071 - dist_relevant_mae: 0.6830 - dist_relevant_mse: 1.6596 - val_loss: 0.1963 - val_prob_loss: 0.0535 - val_dist_loss: 0.7137 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7135 - val_dist_relevant_mse: 1.6924\n",
      "Epoch 312/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2126 - prob_loss: 0.0763 - dist_loss: 0.6819 - prob_kld: 0.0072 - dist_relevant_mae: 0.6816 - dist_relevant_mse: 1.6117 - val_loss: 0.1942 - val_prob_loss: 0.0534 - val_dist_loss: 0.7039 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7036 - val_dist_relevant_mse: 1.6504\n",
      "Epoch 313/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2164 - prob_loss: 0.0802 - dist_loss: 0.6811 - prob_kld: 0.0078 - dist_relevant_mae: 0.6809 - dist_relevant_mse: 1.5834 - val_loss: 0.1953 - val_prob_loss: 0.0534 - val_dist_loss: 0.7092 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7090 - val_dist_relevant_mse: 1.6528\n",
      "Epoch 314/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2100 - prob_loss: 0.0757 - dist_loss: 0.6718 - prob_kld: 0.0071 - dist_relevant_mae: 0.6715 - dist_relevant_mse: 1.6062 - val_loss: 0.1930 - val_prob_loss: 0.0533 - val_dist_loss: 0.6988 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6986 - val_dist_relevant_mse: 1.6058\n",
      "Epoch 315/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2187 - prob_loss: 0.0832 - dist_loss: 0.6778 - prob_kld: 0.0077 - dist_relevant_mae: 0.6776 - dist_relevant_mse: 1.6186 - val_loss: 0.1947 - val_prob_loss: 0.0533 - val_dist_loss: 0.7070 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7067 - val_dist_relevant_mse: 1.6315\n",
      "Epoch 316/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2137 - prob_loss: 0.0779 - dist_loss: 0.6792 - prob_kld: 0.0074 - dist_relevant_mae: 0.6789 - dist_relevant_mse: 1.6215 - val_loss: 0.1970 - val_prob_loss: 0.0536 - val_dist_loss: 0.7169 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7166 - val_dist_relevant_mse: 1.6822\n",
      "Epoch 317/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2159 - prob_loss: 0.0812 - dist_loss: 0.6733 - prob_kld: 0.0076 - dist_relevant_mae: 0.6731 - dist_relevant_mse: 1.6003 - val_loss: 0.1936 - val_prob_loss: 0.0533 - val_dist_loss: 0.7016 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7013 - val_dist_relevant_mse: 1.6053\n",
      "Epoch 318/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2215 - prob_loss: 0.0828 - dist_loss: 0.6939 - prob_kld: 0.0080 - dist_relevant_mae: 0.6936 - dist_relevant_mse: 1.6691 - val_loss: 0.1941 - val_prob_loss: 0.0535 - val_dist_loss: 0.7032 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7029 - val_dist_relevant_mse: 1.6299\n",
      "Epoch 319/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2137 - prob_loss: 0.0795 - dist_loss: 0.6710 - prob_kld: 0.0076 - dist_relevant_mae: 0.6708 - dist_relevant_mse: 1.5913 - val_loss: 0.1936 - val_prob_loss: 0.0535 - val_dist_loss: 0.7009 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7007 - val_dist_relevant_mse: 1.6290\n",
      "Epoch 320/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2086 - prob_loss: 0.0757 - dist_loss: 0.6644 - prob_kld: 0.0071 - dist_relevant_mae: 0.6641 - dist_relevant_mse: 1.5762 - val_loss: 0.1949 - val_prob_loss: 0.0535 - val_dist_loss: 0.7068 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7066 - val_dist_relevant_mse: 1.6670\n",
      "Epoch 321/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2220 - prob_loss: 0.0823 - dist_loss: 0.6986 - prob_kld: 0.0078 - dist_relevant_mae: 0.6984 - dist_relevant_mse: 1.6999 - val_loss: 0.1947 - val_prob_loss: 0.0535 - val_dist_loss: 0.7061 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7059 - val_dist_relevant_mse: 1.6191\n",
      "Epoch 322/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2118 - prob_loss: 0.0746 - dist_loss: 0.6861 - prob_kld: 0.0071 - dist_relevant_mae: 0.6858 - dist_relevant_mse: 1.6554 - val_loss: 0.1932 - val_prob_loss: 0.0534 - val_dist_loss: 0.6988 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6986 - val_dist_relevant_mse: 1.6157\n",
      "Epoch 323/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2180 - prob_loss: 0.0835 - dist_loss: 0.6724 - prob_kld: 0.0079 - dist_relevant_mae: 0.6721 - dist_relevant_mse: 1.6278 - val_loss: 0.1943 - val_prob_loss: 0.0533 - val_dist_loss: 0.7051 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7049 - val_dist_relevant_mse: 1.6219\n",
      "Epoch 324/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2051 - prob_loss: 0.0723 - dist_loss: 0.6640 - prob_kld: 0.0067 - dist_relevant_mae: 0.6637 - dist_relevant_mse: 1.5579 - val_loss: 0.1978 - val_prob_loss: 0.0536 - val_dist_loss: 0.7210 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7208 - val_dist_relevant_mse: 1.6888\n",
      "Epoch 325/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2154 - prob_loss: 0.0796 - dist_loss: 0.6788 - prob_kld: 0.0076 - dist_relevant_mae: 0.6785 - dist_relevant_mse: 1.6300 - val_loss: 0.1922 - val_prob_loss: 0.0531 - val_dist_loss: 0.6955 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6953 - val_dist_relevant_mse: 1.6003\n",
      "Epoch 326/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2180 - prob_loss: 0.0854 - dist_loss: 0.6630 - prob_kld: 0.0079 - dist_relevant_mae: 0.6627 - dist_relevant_mse: 1.5639 - val_loss: 0.1923 - val_prob_loss: 0.0533 - val_dist_loss: 0.6951 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6949 - val_dist_relevant_mse: 1.5926\n",
      "Epoch 327/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2088 - prob_loss: 0.0754 - dist_loss: 0.6674 - prob_kld: 0.0070 - dist_relevant_mae: 0.6672 - dist_relevant_mse: 1.5698 - val_loss: 0.1935 - val_prob_loss: 0.0533 - val_dist_loss: 0.7013 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7011 - val_dist_relevant_mse: 1.6047\n",
      "Epoch 328/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2106 - prob_loss: 0.0763 - dist_loss: 0.6716 - prob_kld: 0.0073 - dist_relevant_mae: 0.6713 - dist_relevant_mse: 1.6255 - val_loss: 0.1920 - val_prob_loss: 0.0531 - val_dist_loss: 0.6945 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6943 - val_dist_relevant_mse: 1.6022\n",
      "Epoch 329/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2091 - prob_loss: 0.0750 - dist_loss: 0.6701 - prob_kld: 0.0072 - dist_relevant_mae: 0.6699 - dist_relevant_mse: 1.5862 - val_loss: 0.1956 - val_prob_loss: 0.0534 - val_dist_loss: 0.7109 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7106 - val_dist_relevant_mse: 1.6725\n",
      "Epoch 330/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2136 - prob_loss: 0.0793 - dist_loss: 0.6718 - prob_kld: 0.0075 - dist_relevant_mae: 0.6716 - dist_relevant_mse: 1.5622 - val_loss: 0.1937 - val_prob_loss: 0.0532 - val_dist_loss: 0.7024 - val_prob_kld: 0.0056 - val_dist_relevant_mae: 0.7022 - val_dist_relevant_mse: 1.6223\n",
      "Epoch 331/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2151 - prob_loss: 0.0809 - dist_loss: 0.6711 - prob_kld: 0.0076 - dist_relevant_mae: 0.6709 - dist_relevant_mse: 1.5811 - val_loss: 0.1921 - val_prob_loss: 0.0531 - val_dist_loss: 0.6950 - val_prob_kld: 0.0055 - val_dist_relevant_mae: 0.6947 - val_dist_relevant_mse: 1.5906\n",
      "Epoch 332/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2124 - prob_loss: 0.0775 - dist_loss: 0.6745 - prob_kld: 0.0072 - dist_relevant_mae: 0.6742 - dist_relevant_mse: 1.5982 - val_loss: 0.1986 - val_prob_loss: 0.0537 - val_dist_loss: 0.7243 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7240 - val_dist_relevant_mse: 1.7121\n",
      "Epoch 333/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2097 - prob_loss: 0.0744 - dist_loss: 0.6765 - prob_kld: 0.0070 - dist_relevant_mae: 0.6763 - dist_relevant_mse: 1.6089 - val_loss: 0.1928 - val_prob_loss: 0.0533 - val_dist_loss: 0.6978 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6976 - val_dist_relevant_mse: 1.6224\n",
      "Epoch 334/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2046 - prob_loss: 0.0739 - dist_loss: 0.6534 - prob_kld: 0.0069 - dist_relevant_mae: 0.6531 - dist_relevant_mse: 1.5177 - val_loss: 0.1969 - val_prob_loss: 0.0533 - val_dist_loss: 0.7177 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7174 - val_dist_relevant_mse: 1.6418\n",
      "Epoch 335/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2152 - prob_loss: 0.0823 - dist_loss: 0.6646 - prob_kld: 0.0078 - dist_relevant_mae: 0.6644 - dist_relevant_mse: 1.5464 - val_loss: 0.1924 - val_prob_loss: 0.0533 - val_dist_loss: 0.6959 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6957 - val_dist_relevant_mse: 1.6093\n",
      "Epoch 336/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2105 - prob_loss: 0.0780 - dist_loss: 0.6626 - prob_kld: 0.0072 - dist_relevant_mae: 0.6623 - dist_relevant_mse: 1.5486 - val_loss: 0.1939 - val_prob_loss: 0.0533 - val_dist_loss: 0.7028 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7026 - val_dist_relevant_mse: 1.6299\n",
      "Epoch 337/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2077 - prob_loss: 0.0741 - dist_loss: 0.6682 - prob_kld: 0.0069 - dist_relevant_mae: 0.6680 - dist_relevant_mse: 1.5667 - val_loss: 0.1960 - val_prob_loss: 0.0533 - val_dist_loss: 0.7136 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7134 - val_dist_relevant_mse: 1.6712\n",
      "Epoch 338/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2050 - prob_loss: 0.0729 - dist_loss: 0.6604 - prob_kld: 0.0068 - dist_relevant_mae: 0.6602 - dist_relevant_mse: 1.5422 - val_loss: 0.1941 - val_prob_loss: 0.0534 - val_dist_loss: 0.7034 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7032 - val_dist_relevant_mse: 1.6520\n",
      "Epoch 339/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2071 - prob_loss: 0.0745 - dist_loss: 0.6632 - prob_kld: 0.0070 - dist_relevant_mae: 0.6629 - dist_relevant_mse: 1.5714 - val_loss: 0.1937 - val_prob_loss: 0.0532 - val_dist_loss: 0.7023 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7020 - val_dist_relevant_mse: 1.6159\n",
      "Epoch 340/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2203 - prob_loss: 0.0847 - dist_loss: 0.6782 - prob_kld: 0.0079 - dist_relevant_mae: 0.6780 - dist_relevant_mse: 1.6258 - val_loss: 0.1941 - val_prob_loss: 0.0534 - val_dist_loss: 0.7039 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7036 - val_dist_relevant_mse: 1.6407\n",
      "Epoch 341/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2166 - prob_loss: 0.0823 - dist_loss: 0.6715 - prob_kld: 0.0076 - dist_relevant_mae: 0.6712 - dist_relevant_mse: 1.6034 - val_loss: 0.1944 - val_prob_loss: 0.0534 - val_dist_loss: 0.7046 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7044 - val_dist_relevant_mse: 1.6428\n",
      "Epoch 342/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2224 - prob_loss: 0.0881 - dist_loss: 0.6717 - prob_kld: 0.0081 - dist_relevant_mae: 0.6714 - dist_relevant_mse: 1.6313 - val_loss: 0.1929 - val_prob_loss: 0.0534 - val_dist_loss: 0.6976 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6974 - val_dist_relevant_mse: 1.6156\n",
      "Epoch 343/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2107 - prob_loss: 0.0786 - dist_loss: 0.6606 - prob_kld: 0.0072 - dist_relevant_mae: 0.6604 - dist_relevant_mse: 1.5447 - val_loss: 0.1938 - val_prob_loss: 0.0534 - val_dist_loss: 0.7018 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7016 - val_dist_relevant_mse: 1.6008\n",
      "Epoch 344/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 172s 2s/step - loss: 0.2108 - prob_loss: 0.0759 - dist_loss: 0.6747 - prob_kld: 0.0072 - dist_relevant_mae: 0.6745 - dist_relevant_mse: 1.5813 - val_loss: 0.1940 - val_prob_loss: 0.0535 - val_dist_loss: 0.7027 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7025 - val_dist_relevant_mse: 1.6209\n",
      "Epoch 345/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2103 - prob_loss: 0.0764 - dist_loss: 0.6694 - prob_kld: 0.0070 - dist_relevant_mae: 0.6692 - dist_relevant_mse: 1.5737 - val_loss: 0.1960 - val_prob_loss: 0.0536 - val_dist_loss: 0.7122 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7120 - val_dist_relevant_mse: 1.6540\n",
      "Epoch 346/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2087 - prob_loss: 0.0735 - dist_loss: 0.6763 - prob_kld: 0.0069 - dist_relevant_mae: 0.6760 - dist_relevant_mse: 1.6027 - val_loss: 0.1954 - val_prob_loss: 0.0535 - val_dist_loss: 0.7097 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7095 - val_dist_relevant_mse: 1.6336\n",
      "Epoch 347/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2197 - prob_loss: 0.0844 - dist_loss: 0.6764 - prob_kld: 0.0081 - dist_relevant_mae: 0.6761 - dist_relevant_mse: 1.6049 - val_loss: 0.1958 - val_prob_loss: 0.0536 - val_dist_loss: 0.7110 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7108 - val_dist_relevant_mse: 1.6545\n",
      "Epoch 348/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2116 - prob_loss: 0.0779 - dist_loss: 0.6684 - prob_kld: 0.0073 - dist_relevant_mae: 0.6682 - dist_relevant_mse: 1.5791 - val_loss: 0.1962 - val_prob_loss: 0.0536 - val_dist_loss: 0.7131 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7128 - val_dist_relevant_mse: 1.6324\n",
      "Epoch 349/400\n",
      " 99/100 [============================>.] - ETA: 1s - loss: 0.2084 - prob_loss: 0.0755 - dist_loss: 0.6648 - prob_kld: 0.0069 - dist_relevant_mae: 0.6645 - dist_relevant_mse: 1.5805\n",
      "Epoch 00349: ReduceLROnPlateau reducing learning rate to 3.7500001781154424e-05.\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2100 - prob_loss: 0.0771 - dist_loss: 0.6648 - prob_kld: 0.0071 - dist_relevant_mae: 0.6646 - dist_relevant_mse: 1.5789 - val_loss: 0.1936 - val_prob_loss: 0.0534 - val_dist_loss: 0.7010 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7008 - val_dist_relevant_mse: 1.6055\n",
      "Epoch 350/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2099 - prob_loss: 0.0775 - dist_loss: 0.6620 - prob_kld: 0.0071 - dist_relevant_mae: 0.6618 - dist_relevant_mse: 1.5464 - val_loss: 0.1944 - val_prob_loss: 0.0535 - val_dist_loss: 0.7047 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7044 - val_dist_relevant_mse: 1.6212\n",
      "Epoch 351/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2163 - prob_loss: 0.0827 - dist_loss: 0.6682 - prob_kld: 0.0076 - dist_relevant_mae: 0.6680 - dist_relevant_mse: 1.6010 - val_loss: 0.1932 - val_prob_loss: 0.0535 - val_dist_loss: 0.6986 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.6984 - val_dist_relevant_mse: 1.6074\n",
      "Epoch 352/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2121 - prob_loss: 0.0798 - dist_loss: 0.6614 - prob_kld: 0.0074 - dist_relevant_mae: 0.6611 - dist_relevant_mse: 1.5197 - val_loss: 0.1930 - val_prob_loss: 0.0533 - val_dist_loss: 0.6985 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6983 - val_dist_relevant_mse: 1.6084\n",
      "Epoch 353/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2077 - prob_loss: 0.0757 - dist_loss: 0.6600 - prob_kld: 0.0070 - dist_relevant_mae: 0.6598 - dist_relevant_mse: 1.5436 - val_loss: 0.1971 - val_prob_loss: 0.0537 - val_dist_loss: 0.7170 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7168 - val_dist_relevant_mse: 1.6705\n",
      "Epoch 354/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2080 - prob_loss: 0.0767 - dist_loss: 0.6562 - prob_kld: 0.0070 - dist_relevant_mae: 0.6560 - dist_relevant_mse: 1.5196 - val_loss: 0.1938 - val_prob_loss: 0.0534 - val_dist_loss: 0.7023 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7020 - val_dist_relevant_mse: 1.6175\n",
      "Epoch 355/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2116 - prob_loss: 0.0800 - dist_loss: 0.6577 - prob_kld: 0.0072 - dist_relevant_mae: 0.6574 - dist_relevant_mse: 1.5471 - val_loss: 0.1959 - val_prob_loss: 0.0536 - val_dist_loss: 0.7117 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7115 - val_dist_relevant_mse: 1.6512\n",
      "Epoch 356/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2114 - prob_loss: 0.0794 - dist_loss: 0.6598 - prob_kld: 0.0070 - dist_relevant_mae: 0.6596 - dist_relevant_mse: 1.5699 - val_loss: 0.1954 - val_prob_loss: 0.0535 - val_dist_loss: 0.7097 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7095 - val_dist_relevant_mse: 1.6496\n",
      "Epoch 357/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2164 - prob_loss: 0.0827 - dist_loss: 0.6687 - prob_kld: 0.0075 - dist_relevant_mae: 0.6684 - dist_relevant_mse: 1.6413 - val_loss: 0.1960 - val_prob_loss: 0.0536 - val_dist_loss: 0.7118 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7116 - val_dist_relevant_mse: 1.6577\n",
      "Epoch 358/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2053 - prob_loss: 0.0737 - dist_loss: 0.6578 - prob_kld: 0.0068 - dist_relevant_mae: 0.6576 - dist_relevant_mse: 1.5599 - val_loss: 0.1955 - val_prob_loss: 0.0536 - val_dist_loss: 0.7099 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7097 - val_dist_relevant_mse: 1.6535\n",
      "Epoch 359/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2012 - prob_loss: 0.0694 - dist_loss: 0.6594 - prob_kld: 0.0062 - dist_relevant_mae: 0.6592 - dist_relevant_mse: 1.6146 - val_loss: 0.1946 - val_prob_loss: 0.0535 - val_dist_loss: 0.7054 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7051 - val_dist_relevant_mse: 1.6319\n",
      "Epoch 360/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2090 - prob_loss: 0.0777 - dist_loss: 0.6565 - prob_kld: 0.0071 - dist_relevant_mae: 0.6563 - dist_relevant_mse: 1.5536 - val_loss: 0.1951 - val_prob_loss: 0.0535 - val_dist_loss: 0.7082 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7079 - val_dist_relevant_mse: 1.6289\n",
      "Epoch 361/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2099 - prob_loss: 0.0770 - dist_loss: 0.6645 - prob_kld: 0.0071 - dist_relevant_mae: 0.6643 - dist_relevant_mse: 1.5689 - val_loss: 0.1946 - val_prob_loss: 0.0534 - val_dist_loss: 0.7057 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7055 - val_dist_relevant_mse: 1.6371\n",
      "Epoch 362/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2116 - prob_loss: 0.0792 - dist_loss: 0.6619 - prob_kld: 0.0071 - dist_relevant_mae: 0.6617 - dist_relevant_mse: 1.5633 - val_loss: 0.1981 - val_prob_loss: 0.0537 - val_dist_loss: 0.7221 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7219 - val_dist_relevant_mse: 1.6804\n",
      "Epoch 363/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2100 - prob_loss: 0.0783 - dist_loss: 0.6588 - prob_kld: 0.0073 - dist_relevant_mae: 0.6586 - dist_relevant_mse: 1.5297 - val_loss: 0.1937 - val_prob_loss: 0.0535 - val_dist_loss: 0.7012 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7010 - val_dist_relevant_mse: 1.6167\n",
      "Epoch 364/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2062 - prob_loss: 0.0764 - dist_loss: 0.6489 - prob_kld: 0.0069 - dist_relevant_mae: 0.6487 - dist_relevant_mse: 1.5149 - val_loss: 0.1930 - val_prob_loss: 0.0533 - val_dist_loss: 0.6987 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6985 - val_dist_relevant_mse: 1.5995\n",
      "Epoch 365/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2053 - prob_loss: 0.0727 - dist_loss: 0.6630 - prob_kld: 0.0068 - dist_relevant_mae: 0.6628 - dist_relevant_mse: 1.5593 - val_loss: 0.1935 - val_prob_loss: 0.0533 - val_dist_loss: 0.7006 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.7004 - val_dist_relevant_mse: 1.6092\n",
      "Epoch 366/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 171s 2s/step - loss: 0.2054 - prob_loss: 0.0746 - dist_loss: 0.6542 - prob_kld: 0.0067 - dist_relevant_mae: 0.6540 - dist_relevant_mse: 1.5242 - val_loss: 0.1931 - val_prob_loss: 0.0533 - val_dist_loss: 0.6992 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6990 - val_dist_relevant_mse: 1.6139\n",
      "Epoch 367/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2123 - prob_loss: 0.0790 - dist_loss: 0.6667 - prob_kld: 0.0071 - dist_relevant_mae: 0.6664 - dist_relevant_mse: 1.5571 - val_loss: 0.1956 - val_prob_loss: 0.0535 - val_dist_loss: 0.7108 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7106 - val_dist_relevant_mse: 1.6432\n",
      "Epoch 368/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2064 - prob_loss: 0.0753 - dist_loss: 0.6553 - prob_kld: 0.0070 - dist_relevant_mae: 0.6551 - dist_relevant_mse: 1.5092 - val_loss: 0.1944 - val_prob_loss: 0.0535 - val_dist_loss: 0.7049 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7047 - val_dist_relevant_mse: 1.6192\n",
      "Epoch 369/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2053 - prob_loss: 0.0770 - dist_loss: 0.6416 - prob_kld: 0.0070 - dist_relevant_mae: 0.6414 - dist_relevant_mse: 1.4719 - val_loss: 0.1932 - val_prob_loss: 0.0533 - val_dist_loss: 0.6995 - val_prob_kld: 0.0057 - val_dist_relevant_mae: 0.6993 - val_dist_relevant_mse: 1.6037\n",
      "Epoch 370/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2028 - prob_loss: 0.0731 - dist_loss: 0.6486 - prob_kld: 0.0065 - dist_relevant_mae: 0.6483 - dist_relevant_mse: 1.5146 - val_loss: 0.1929 - val_prob_loss: 0.0534 - val_dist_loss: 0.6973 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6971 - val_dist_relevant_mse: 1.5987\n",
      "Epoch 371/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2100 - prob_loss: 0.0789 - dist_loss: 0.6553 - prob_kld: 0.0072 - dist_relevant_mae: 0.6551 - dist_relevant_mse: 1.5738 - val_loss: 0.1930 - val_prob_loss: 0.0534 - val_dist_loss: 0.6981 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6978 - val_dist_relevant_mse: 1.5921\n",
      "Epoch 372/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2040 - prob_loss: 0.0733 - dist_loss: 0.6534 - prob_kld: 0.0066 - dist_relevant_mae: 0.6532 - dist_relevant_mse: 1.5334 - val_loss: 0.1930 - val_prob_loss: 0.0534 - val_dist_loss: 0.6983 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6981 - val_dist_relevant_mse: 1.5955\n",
      "Epoch 373/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2102 - prob_loss: 0.0792 - dist_loss: 0.6548 - prob_kld: 0.0071 - dist_relevant_mae: 0.6545 - dist_relevant_mse: 1.5368 - val_loss: 0.1932 - val_prob_loss: 0.0534 - val_dist_loss: 0.6988 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6986 - val_dist_relevant_mse: 1.6030\n",
      "Epoch 374/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2087 - prob_loss: 0.0792 - dist_loss: 0.6478 - prob_kld: 0.0071 - dist_relevant_mae: 0.6475 - dist_relevant_mse: 1.4960 - val_loss: 0.1946 - val_prob_loss: 0.0534 - val_dist_loss: 0.7055 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7053 - val_dist_relevant_mse: 1.6219\n",
      "Epoch 375/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2042 - prob_loss: 0.0724 - dist_loss: 0.6591 - prob_kld: 0.0063 - dist_relevant_mae: 0.6589 - dist_relevant_mse: 1.5604 - val_loss: 0.1958 - val_prob_loss: 0.0537 - val_dist_loss: 0.7106 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7104 - val_dist_relevant_mse: 1.6343\n",
      "Epoch 376/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2043 - prob_loss: 0.0735 - dist_loss: 0.6543 - prob_kld: 0.0066 - dist_relevant_mae: 0.6540 - dist_relevant_mse: 1.5223 - val_loss: 0.1945 - val_prob_loss: 0.0535 - val_dist_loss: 0.7051 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7048 - val_dist_relevant_mse: 1.6258\n",
      "Epoch 377/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2115 - prob_loss: 0.0795 - dist_loss: 0.6600 - prob_kld: 0.0073 - dist_relevant_mae: 0.6598 - dist_relevant_mse: 1.5299 - val_loss: 0.1956 - val_prob_loss: 0.0536 - val_dist_loss: 0.7101 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7098 - val_dist_relevant_mse: 1.6571\n",
      "Epoch 378/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2100 - prob_loss: 0.0791 - dist_loss: 0.6543 - prob_kld: 0.0070 - dist_relevant_mae: 0.6541 - dist_relevant_mse: 1.5166 - val_loss: 0.1942 - val_prob_loss: 0.0534 - val_dist_loss: 0.7039 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.7037 - val_dist_relevant_mse: 1.6202\n",
      "Epoch 379/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2085 - prob_loss: 0.0768 - dist_loss: 0.6584 - prob_kld: 0.0068 - dist_relevant_mae: 0.6582 - dist_relevant_mse: 1.5559 - val_loss: 0.1931 - val_prob_loss: 0.0534 - val_dist_loss: 0.6984 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6982 - val_dist_relevant_mse: 1.5988\n",
      "Epoch 380/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2041 - prob_loss: 0.0730 - dist_loss: 0.6554 - prob_kld: 0.0064 - dist_relevant_mae: 0.6552 - dist_relevant_mse: 1.5559 - val_loss: 0.1939 - val_prob_loss: 0.0535 - val_dist_loss: 0.7016 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7013 - val_dist_relevant_mse: 1.6221\n",
      "Epoch 381/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2039 - prob_loss: 0.0736 - dist_loss: 0.6516 - prob_kld: 0.0066 - dist_relevant_mae: 0.6513 - dist_relevant_mse: 1.4962 - val_loss: 0.1958 - val_prob_loss: 0.0536 - val_dist_loss: 0.7109 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7106 - val_dist_relevant_mse: 1.6375\n",
      "Epoch 382/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2041 - prob_loss: 0.0720 - dist_loss: 0.6605 - prob_kld: 0.0066 - dist_relevant_mae: 0.6602 - dist_relevant_mse: 1.5523 - val_loss: 0.1950 - val_prob_loss: 0.0536 - val_dist_loss: 0.7072 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7070 - val_dist_relevant_mse: 1.6380\n",
      "Epoch 383/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2120 - prob_loss: 0.0800 - dist_loss: 0.6601 - prob_kld: 0.0073 - dist_relevant_mae: 0.6599 - dist_relevant_mse: 1.5314 - val_loss: 0.1938 - val_prob_loss: 0.0535 - val_dist_loss: 0.7013 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7010 - val_dist_relevant_mse: 1.6015\n",
      "Epoch 384/400\n",
      "100/100 [==============================] - 170s 2s/step - loss: 0.2159 - prob_loss: 0.0847 - dist_loss: 0.6561 - prob_kld: 0.0075 - dist_relevant_mae: 0.6558 - dist_relevant_mse: 1.5358 - val_loss: 0.1939 - val_prob_loss: 0.0535 - val_dist_loss: 0.7018 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7015 - val_dist_relevant_mse: 1.6159\n",
      "Epoch 385/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2084 - prob_loss: 0.0778 - dist_loss: 0.6530 - prob_kld: 0.0067 - dist_relevant_mae: 0.6528 - dist_relevant_mse: 1.5316 - val_loss: 0.1938 - val_prob_loss: 0.0535 - val_dist_loss: 0.7016 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7014 - val_dist_relevant_mse: 1.6189\n",
      "Epoch 386/400\n",
      "100/100 [==============================] - 170s 2s/step - loss: 0.2079 - prob_loss: 0.0768 - dist_loss: 0.6552 - prob_kld: 0.0069 - dist_relevant_mae: 0.6550 - dist_relevant_mse: 1.5275 - val_loss: 0.1953 - val_prob_loss: 0.0536 - val_dist_loss: 0.7085 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7083 - val_dist_relevant_mse: 1.6434\n",
      "Epoch 387/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2060 - prob_loss: 0.0756 - dist_loss: 0.6518 - prob_kld: 0.0068 - dist_relevant_mae: 0.6516 - dist_relevant_mse: 1.5115 - val_loss: 0.1952 - val_prob_loss: 0.0536 - val_dist_loss: 0.7078 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7075 - val_dist_relevant_mse: 1.6350\n",
      "Epoch 388/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2103 - prob_loss: 0.0790 - dist_loss: 0.6565 - prob_kld: 0.0071 - dist_relevant_mae: 0.6563 - dist_relevant_mse: 1.5385 - val_loss: 0.1959 - val_prob_loss: 0.0537 - val_dist_loss: 0.7112 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7110 - val_dist_relevant_mse: 1.6509\n",
      "Epoch 389/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 99/100 [============================>.] - ETA: 1s - loss: 0.2110 - prob_loss: 0.0801 - dist_loss: 0.6544 - prob_kld: 0.0073 - dist_relevant_mae: 0.6542 - dist_relevant_mse: 1.5395\n",
      "Epoch 00389: ReduceLROnPlateau reducing learning rate to 1.8750000890577212e-05.\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2104 - prob_loss: 0.0796 - dist_loss: 0.6540 - prob_kld: 0.0072 - dist_relevant_mae: 0.6538 - dist_relevant_mse: 1.5399 - val_loss: 0.1949 - val_prob_loss: 0.0536 - val_dist_loss: 0.7067 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7064 - val_dist_relevant_mse: 1.6298\n",
      "Epoch 390/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2093 - prob_loss: 0.0776 - dist_loss: 0.6586 - prob_kld: 0.0071 - dist_relevant_mae: 0.6584 - dist_relevant_mse: 1.5315 - val_loss: 0.1935 - val_prob_loss: 0.0536 - val_dist_loss: 0.6997 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.6995 - val_dist_relevant_mse: 1.6181\n",
      "Epoch 391/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2037 - prob_loss: 0.0741 - dist_loss: 0.6479 - prob_kld: 0.0067 - dist_relevant_mae: 0.6477 - dist_relevant_mse: 1.5115 - val_loss: 0.1939 - val_prob_loss: 0.0536 - val_dist_loss: 0.7018 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7016 - val_dist_relevant_mse: 1.6282\n",
      "Epoch 392/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2111 - prob_loss: 0.0794 - dist_loss: 0.6586 - prob_kld: 0.0071 - dist_relevant_mae: 0.6584 - dist_relevant_mse: 1.5444 - val_loss: 0.1950 - val_prob_loss: 0.0535 - val_dist_loss: 0.7074 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7072 - val_dist_relevant_mse: 1.6382\n",
      "Epoch 393/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2056 - prob_loss: 0.0762 - dist_loss: 0.6467 - prob_kld: 0.0069 - dist_relevant_mae: 0.6464 - dist_relevant_mse: 1.4733 - val_loss: 0.1953 - val_prob_loss: 0.0536 - val_dist_loss: 0.7082 - val_prob_kld: 0.0061 - val_dist_relevant_mae: 0.7079 - val_dist_relevant_mse: 1.6432\n",
      "Epoch 394/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2067 - prob_loss: 0.0775 - dist_loss: 0.6460 - prob_kld: 0.0069 - dist_relevant_mae: 0.6457 - dist_relevant_mse: 1.4613 - val_loss: 0.1954 - val_prob_loss: 0.0536 - val_dist_loss: 0.7091 - val_prob_kld: 0.0060 - val_dist_relevant_mae: 0.7089 - val_dist_relevant_mse: 1.6454\n",
      "Epoch 395/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2064 - prob_loss: 0.0757 - dist_loss: 0.6534 - prob_kld: 0.0068 - dist_relevant_mae: 0.6531 - dist_relevant_mse: 1.5534 - val_loss: 0.1936 - val_prob_loss: 0.0535 - val_dist_loss: 0.7005 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7003 - val_dist_relevant_mse: 1.6171\n",
      "Epoch 396/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2075 - prob_loss: 0.0772 - dist_loss: 0.6512 - prob_kld: 0.0070 - dist_relevant_mae: 0.6509 - dist_relevant_mse: 1.5133 - val_loss: 0.1939 - val_prob_loss: 0.0535 - val_dist_loss: 0.7020 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7018 - val_dist_relevant_mse: 1.6184\n",
      "Epoch 397/400\n",
      "100/100 [==============================] - 171s 2s/step - loss: 0.2089 - prob_loss: 0.0792 - dist_loss: 0.6483 - prob_kld: 0.0070 - dist_relevant_mae: 0.6481 - dist_relevant_mse: 1.5143 - val_loss: 0.1933 - val_prob_loss: 0.0534 - val_dist_loss: 0.6994 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.6992 - val_dist_relevant_mse: 1.6038\n",
      "Epoch 398/400\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.2131 - prob_loss: 0.0826 - dist_loss: 0.6524 - prob_kld: 0.0075 - dist_relevant_mae: 0.6522 - dist_relevant_mse: 1.5105 - val_loss: 0.1934 - val_prob_loss: 0.0534 - val_dist_loss: 0.6999 - val_prob_kld: 0.0058 - val_dist_relevant_mae: 0.6997 - val_dist_relevant_mse: 1.6030\n",
      "Epoch 399/400\n",
      "100/100 [==============================] - 173s 2s/step - loss: 0.2045 - prob_loss: 0.0743 - dist_loss: 0.6510 - prob_kld: 0.0066 - dist_relevant_mae: 0.6508 - dist_relevant_mse: 1.4747 - val_loss: 0.1936 - val_prob_loss: 0.0535 - val_dist_loss: 0.7005 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7003 - val_dist_relevant_mse: 1.6206\n",
      "Epoch 400/400\n",
      "100/100 [==============================] - 170s 2s/step - loss: 0.2066 - prob_loss: 0.0766 - dist_loss: 0.6500 - prob_kld: 0.0069 - dist_relevant_mae: 0.6497 - dist_relevant_mse: 1.4917 - val_loss: 0.1935 - val_prob_loss: 0.0535 - val_dist_loss: 0.7003 - val_prob_kld: 0.0059 - val_dist_relevant_mae: 0.7001 - val_dist_relevant_mse: 1.6221\n",
      "\n",
      "Loading network weights from 'weights_best.h5'.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x16cc77cdb48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    if use_gpu:\n",
    "        from csbdeep.utils.tf import limit_gpu_memory\n",
    "        # adjust as necessary: limit GPU memory to be used by TensorFlow to leave some to OpenCL-based computations\n",
    "        limit_gpu_memory(0.8, total_memory=10000)\n",
    "\n",
    "    model = StarDist3D(conf,\n",
    "                       name=modelname,\n",
    "                       basedir=basedir)\n",
    "\n",
    "    fov = np.array(model._axes_tile_overlap('ZYX'))\n",
    "\n",
    "    median_size = calculate_extents(Y_trn, np.median)\n",
    "\n",
    "    if any(median_size > fov):\n",
    "        print(\"WARNING: median object size larger than field of view of the neural network.\")\n",
    "\n",
    "    model.train(X_trn, Y_trn,\n",
    "                validation_data=(X_vld, Y_vld),\n",
    "                epochs=400,\n",
    "                augmenter=augmenter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NMS threshold = 0.3:  75%|█████████████████████████████████▊           | 15/20 [09:15<03:05, 37.05s/it, 0.623 -> 0.660]\n",
      "NMS threshold = 0.4:  75%|█████████████████████████████████▊           | 15/20 [07:21<02:27, 29.41s/it, 0.714 -> 0.646]\n",
      "NMS threshold = 0.5:  75%|█████████████████████████████████▊           | 15/20 [08:18<02:46, 33.24s/it, 0.730 -> 0.625]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using optimized values: prob_thresh=0.623021, nms_thresh=0.3.\n",
      "Saving to 'thresholds.json'.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'prob': 0.6230211612633794, 'nms': 0.3}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.optimize_thresholds(X_vld, Y_vld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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