dense Unet with attention head.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import skimage, os\n",
    "from skimage.morphology import ball, disk, dilation, binary_erosion, remove_small_objects, erosion, closing, reconstruction, binary_closing\n",
    "from skimage.measure import label,regionprops, perimeter\n",
    "from skimage.morphology import binary_dilation, binary_opening\n",
    "from skimage.filters import roberts, sobel\n",
    "from skimage import measure, feature\n",
    "from skimage.segmentation import clear_border\n",
    "from skimage import data\n",
    "from scipy import ndimage as ndi\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "import scipy.misc\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from skimage.io import imread\n",
    "from livelossplot import PlotLossesKerasTF\n",
    "from functools import *\n",
    "import elasticdeform\n",
    "\n",
    "import random\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='1'\n",
    "\n",
    "BASE_IMG_PATH=os.path.join('hecktor-master','data','resampled')\n",
    "BATCH_SIZE = 3\n",
    "INPUT_SHAPE = (144,144,144,2)\n",
    "PROJECT_NAME = 'hector'\n",
    "AUG_RATE = 0.3\n",
    "\n",
    "\n",
    "random.seed(42)\n",
    "random_seed = 42\n",
    "\n",
    "AUGMENTATION_OPS = [\n",
    "    lambda t: np.rot90(t, axes=(1, 2), k=1),  # 90 degree rotation\n",
    "    lambda t: np.rot90(t, axes=(1, 2), k=2),  # 180 degree rotation\n",
    "    lambda t: np.rot90(t, axes=(1, 2), k=3),  # 270 degree rotation\n",
    "    lambda t: np.flip(t, axis=0),  # Axial flip\n",
    "    lambda t: np.flip(t, axis=1),  # Sagittal flip\n",
    "    lambda t: np.flip(t, axis=2)  # Coronal flip\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'hecktor-master/data/resampled/CHGJ035_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ082_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS069_ct_gtvt.nii.gz',\n",
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       " 'hecktor-master/data/resampled/CHUS100_pt.nii.gz',\n",
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       " 'hecktor-master/data/resampled/CHUS036_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS010_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS055_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR034_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ037_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ025_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS003_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM012_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ053_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM015_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ091_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM046_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR001_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS069_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM013_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ031_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS080_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS086_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ076_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM063_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM033_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ015_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ085_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS073_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS089_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM013_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS066_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ016_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS006_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS040_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS038_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS035_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM010_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR034_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM064_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ039_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS053_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS045_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS004_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM006_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM033_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ071_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ085_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM041_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM057_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR016_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR029_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM043_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM021_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ025_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR014_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ018_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ077_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS064_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS042_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS090_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ057_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS016_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR020_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ029_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS083_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ087_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS027_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM002_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ039_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ080_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS097_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM040_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM054_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS086_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM050_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS061_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS053_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS050_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS028_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ007_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM061_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR023_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ067_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS028_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ043_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ028_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM008_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ082_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM063_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS051_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS094_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM042_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS061_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS047_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ091_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ065_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ028_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ089_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ067_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS057_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ038_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM026_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS067_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ010_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM002_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM018_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR001_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ013_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHMR028_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ078_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ072_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS080_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM002_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM065_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM054_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUS022_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHUM032_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ043_ct.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ062_ct_gtvt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ010_pt.nii.gz',\n",
       " 'hecktor-master/data/resampled/CHGJ073_ct_gtvt.nii.gz']"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob(os.path.join(BASE_IMG_PATH,'*'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "201  matching files found: hecktor-master/data/resampled/CHUS074_ct.nii.gz hecktor-master/data/resampled/CHUS074_pt.nii.gz\n",
      "201  matching files found: hecktor-master/data/resampled/CHUS074_ct.nii.gz hecktor-master/data/resampled/CHUS074_ct_gtvt.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# show some of the files\n",
    "all_ct_images= glob(os.path.join(BASE_IMG_PATH,'*ct.nii.gz'))\n",
    "all_pt_images = [x.replace('ct.nii.gz', 'pt.nii.gz') for x in all_ct_images]\n",
    "all_mask_images = [x.replace('ct.nii.gz', 'ct_gtvt.nii.gz') for x in all_ct_images]\n",
    "np.random.seed(42)\n",
    "rand_index = np.random.randint(0, len(all_ct_images))\n",
    "print(len(all_ct_images),' matching files found:',all_ct_images[rand_index], all_pt_images[rand_index])\n",
    "print(len(all_mask_images),' matching files found:',all_ct_images[rand_index], all_mask_images[rand_index])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(144, 144, 144)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mask')"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "try:\n",
    "    import nibabel as nib\n",
    "except:\n",
    "    raise ImportError('Install NIBABEL')\n",
    "    \n",
    "test_ct=np.asarray(nib.load(all_ct_images[rand_index]).dataobj)\n",
    "test_pt=np.asarray(nib.load(all_pt_images[rand_index]).dataobj)\n",
    "test_mask=np.asarray(nib.load(all_mask_images[rand_index]).dataobj)\n",
    "print(test_ct.shape)\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize = (12, 6))\n",
    "\n",
    "ax1.imshow(test_ct[test_ct.shape[0]//2], cmap = 'gray')\n",
    "ax1.set_title('CT')\n",
    "ax2.imshow(test_pt[test_pt.shape[0]//2], cmap = 'gray')\n",
    "ax2.set_title('PT')\n",
    "ax3.imshow(test_mask[test_mask.shape[0]//2], cmap = 'gray')\n",
    "ax3.set_title('Mask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.489239 -0.0050189 (144, 144, 144)\n",
      "9.617565 -0.9385557 (144, 144, 144)\n"
     ]
    }
   ],
   "source": [
    "def make144(image):\n",
    "    new_image = image[:144,:144,:144]\n",
    "    return new_image\n",
    "\n",
    "def preprocess(image):\n",
    "    if image.shape != (144,144,144):\n",
    "        image = make144(image)\n",
    "    new_image = (image - image.mean())/image.std()\n",
    "    \n",
    "    return new_image\n",
    "print(test_pt.max(), test_pt.min(),test_pt.shape)\n",
    "new_img = preprocess(test_pt)\n",
    "print(new_img.max(), new_img.min(),new_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 144, 288)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = preprocess(test_pt)\n",
    "data = np.concatenate((data, preprocess(test_pt)), -1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check if ct, pt, mask have same numbers.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "if not len(all_ct_images) == len(all_pt_images) == len(all_mask_images):\n",
    "    print(len(all_ct_images), len(all_pt_images), len(all_mask_images))\n",
    "    raise ValueError('ct,pt,mask number not match')\n",
    "\n",
    "data_list_index = range(len(all_ct_images))\n",
    "train_data_list, val_data_list = train_test_split(data_list_index, test_size = 0.2, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import *\n",
    "\n",
    "def get_augment_operations(random_state, probability=.25):\n",
    "    \"\"\"\n",
    "    Return a function that can be applied for augmenting a tensor.\n",
    "    Included operations are:\n",
    "        - flipping (axial, sagittal and coronal);\n",
    "        - rotation (90, 180 and 720).\n",
    "    Each of the six operations has a certain probability to be included for \n",
    "    augmentation.\n",
    "    :param random_state: random state.\n",
    "    :param probability: probability for operation inclusion.\n",
    "    :return: function to apply to tensor.\n",
    "    \"\"\"\n",
    "    probabilities = random_state.uniform(size=len(AUGMENTATION_OPS))\n",
    "    operations = [\n",
    "        f for p, f in zip(probabilities, AUGMENTATION_OPS) if p > probability\n",
    "    ]\n",
    "\n",
    "    def _wrapped(x):\n",
    "        augmented = reduce(lambda t, f: f(t), operations, x)\n",
    "        assert augmented.shape == x.shape\n",
    "        return augmented\n",
    "\n",
    "    return _wrapped\n",
    "\n",
    "# Augment\n",
    "#f = get_augment_operations(random_state, augment_probability)\n",
    "#x = f(x)\n",
    "#y = f(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Augment test\n",
    "\n",
    "random_state = np.random.RandomState()\n",
    "f = get_augment_operations(random_state)\n",
    "\n",
    "_aug_test_ct = f(test_ct)\n",
    "_aug_test_pt = f(test_pt)\n",
    "_aug_test_mask = f(test_mask)\n",
    "_aug_test_ct[_aug_test_ct<-150]=-150\n",
    "_aug_test_ct[_aug_test_ct>150]=150\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize = (12, 6))\n",
    "\n",
    "ax1.imshow(_aug_test_ct[_aug_test_ct.shape[0]//2], cmap = 'gray')\n",
    "ax1.set_title('CT')\n",
    "ax2.imshow(_aug_test_pt[_aug_test_pt.shape[0]//2], cmap = 'gray')\n",
    "ax2.set_title('PT')\n",
    "ax3.imshow(_aug_test_mask[_aug_test_mask.shape[0]//2], cmap = 'gray')\n",
    "ax3.set_title('Mask')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n",
      "(3, 144, 144, 144)\n",
      "testing valid pipeline\n",
      "(3, 144, 144, 144, 2) (3, 144, 144, 144)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import random\n",
    "from scipy.ndimage import zoom\n",
    "def tf_data_generator(file_list, batch_size = 2, augment_rate = 0):\n",
    "\n",
    "    i = 0\n",
    "    j = 0\n",
    "    if augment_rate!=0:\n",
    "        #if we want to augmenation\n",
    "        k = int(len(file_list) * augment_rate)\n",
    "        indicies = random.sample(range(len(file_list)), k)\n",
    "        aug_list = [file_list[i] for i in indicies]\n",
    "            \n",
    "    while True:\n",
    "        if i*batch_size >= (len(file_list) * (1 + augment_rate)): \n",
    "            #start again\n",
    "            i = 0\n",
    "            j = 0\n",
    "            np.random.shuffle(file_list)\n",
    "            \n",
    "        if augment_rate != 0 and i*batch_size >= len(file_list):  \n",
    "            #augmentation\n",
    "            # if data in augmentation list \n",
    "            file_chunk = aug_list[j*batch_size:(j+1)*batch_size] \n",
    "            data = []\n",
    "            labels = []\n",
    "            for file in file_chunk:   \n",
    "                _ct=np.asarray(nib.load(all_ct_images[file]).dataobj)\n",
    "                _pt=np.asarray(nib.load(all_pt_images[file]).dataobj)\n",
    "                _mask=np.asarray(nib.load(all_mask_images[file]).dataobj)\n",
    "                _ct[_ct<-150] = -150\n",
    "                _ct[_ct>150] = 150\n",
    "\n",
    "                _ct = preprocess(_ct)\n",
    "                _pt = preprocess(_pt)\n",
    "                _mask = make144(_mask)\n",
    "                #print(_ct.shape, _pt.shape, _mask.shape)              \n",
    "                if i % 2 == 0 :\n",
    "                    #elastic deformation\n",
    "                    num1, num2 = sorted(random.sample(set([0, 1, 2]), 2))\n",
    "                    [_ct, _pt, _mask] = elasticdeform.deform_random_grid([_ct, _pt, _mask] , sigma=3, points=4, \n",
    "                                                                      axis=[(num1, num2), (num1, num2), (num1, num2)])            \n",
    "                #if i % 3 == 0:\n",
    "                #    r = random.randrange(10, 13, 1)\n",
    "                #    r = r/10\n",
    "                #    _ct = make144(zoom(_ct, (r, r, r), mode = 'nearest'))\n",
    "                #    _pt = make144(zoom(_pt, (r, r, r), mode = 'nearest'))\n",
    "                #    _mask = make144(zoom(_mask, (r, r, r), mode = 'nearest'))\n",
    "                #    \n",
    "                #_mask[_mask<0.5] = 0 \n",
    "                #_mask[_mask >= 0.5] = 1\n",
    "                #\n",
    "                #img = np.concatenate((_ct[..., np.newaxis], _pt[..., np.newaxis]), -1)\n",
    "                #\n",
    "                #if i % 1 == 0 :\n",
    "                #    #print(\"rotation, flip deformation\")\n",
    "                #    #rotation, flip augmentation\n",
    "                #    random_state = np.random.RandomState()\n",
    "                #    f = get_augment_operations(random_state)\n",
    "                #    img = f(img)\n",
    "                #    _mask = f(_mask)\n",
    "            \n",
    "                data.append(img) \n",
    "                #binarization again for deformated masks.\n",
    "\n",
    "                labels.append(_mask)\n",
    "                \n",
    "            data = np.asarray(data)\n",
    "            labels = np.asarray(labels)\n",
    "            yield data, labels\n",
    "            i = i + 1\n",
    "            j = j + 1\n",
    "\n",
    "        else:\n",
    "            file_chunk = file_list[i*batch_size:(i+1)*batch_size] \n",
    "            data = []\n",
    "            labels = []\n",
    "            for file in file_chunk:\n",
    "                _ct=np.asarray(nib.load(all_ct_images[file]).dataobj)\n",
    "                _pt=np.asarray(nib.load(all_pt_images[file]).dataobj)\n",
    "                _mask=np.asarray(nib.load(all_mask_images[file]).dataobj)\n",
    "\n",
    "                _ct[_ct<-150] = -150\n",
    "                _ct[_ct>150] =150\n",
    "                \n",
    "                _ct = preprocess(_ct)\n",
    "                _pt = preprocess(_pt)\n",
    "                \n",
    "                img = np.concatenate((_ct[..., np.newaxis], _pt[..., np.newaxis]), -1)\n",
    "                \n",
    "                data.append(img) \n",
    "                _mask = make144(_mask)\n",
    "                \n",
    "                labels.append(_mask)\n",
    "                \n",
    "            data = np.asarray(data)\n",
    "            labels = np.asarray(labels)\n",
    "            yield data, labels\n",
    "            i = i + 1\n",
    "            \n",
    "batch_size = BATCH_SIZE\n",
    "augment_rate = AUG_RATE\n",
    "#tf_data_generator(train_data_list)\n",
    "\n",
    "\n",
    "train_dataset = tf.data.Dataset.from_generator(tf_data_generator,args= [train_data_list, batch_size, augment_rate],\n",
    "                                         output_types = (tf.float32, tf.float32),\n",
    "                                         output_shapes = ((None,144,144,144,2),(None,144,144,144)))        \n",
    "\n",
    "valid_dataset = tf.data.Dataset.from_generator(tf_data_generator,args= [val_data_list, batch_size],\n",
    "                                         output_types = (tf.float32, tf.float32),\n",
    "                                         output_shapes = ((None,144,144,144,2),(None,144,144,144))) \n",
    "\n",
    "#test the data generator\n",
    "num = 0\n",
    "for data, labels in train_dataset:\n",
    "    print(data.shape, labels.shape)\n",
    "    print(labels.shape)\n",
    "    num = num + 1\n",
    "    if num > 7: break\n",
    "print(\"testing valid pipeline\")  \n",
    "for data, labels in valid_dataset:\n",
    "    print(data.shape, labels.shape)\n",
    "\n",
    "    num = num + 1\n",
    "    if num > 0: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = random.randrange(8, 12, 1)\n",
    "r/10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Densely 3D Unet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 144, 144, 14 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv3d (Conv3D)                 (None, 144, 144, 144 2640        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 144, 144, 144 576         conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 144, 144, 144 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_1 (Conv3D)               (None, 144, 144, 144 20752       activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 144, 144, 144 0           conv3d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 144, 144, 144 0           dropout[0][0]                    \n",
      "                                                                 conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 144, 144, 144 576         concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 144, 144, 144 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_2 (Conv3D)               (None, 144, 144, 144 27664       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 144, 144, 144 0           conv3d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 144, 144, 144 0           dropout_1[0][0]                  \n",
      "                                                                 concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 144, 144, 144 576         concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 144, 144, 144 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_3 (Conv3D)               (None, 144, 144, 144 34576       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 144, 144, 144 0           conv3d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 144, 144, 144 0           dropout_2[0][0]                  \n",
      "                                                                 concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 144, 144, 144 576         concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 144, 144, 144 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_4 (Conv3D)               (None, 144, 144, 144 9312        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 144, 144, 144 0           conv3d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_5 (Conv3D)               (None, 72, 72, 144,  9312        dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 72, 72, 144,  288         conv3d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 72, 72, 144,  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_6 (Conv3D)               (None, 72, 72, 144,  41488       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 72, 72, 144,  0           conv3d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 72, 72, 144,  0           dropout_4[0][0]                  \n",
      "                                                                 conv3d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 72, 72, 144,  288         concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 72, 72, 144,  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_7 (Conv3D)               (None, 72, 72, 144,  48400       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 72, 72, 144,  0           conv3d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 72, 72, 144,  0           dropout_5[0][0]                  \n",
      "                                                                 concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 72, 72, 144,  288         concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 72, 72, 144,  0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_8 (Conv3D)               (None, 72, 72, 144,  55312       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 72, 72, 144,  0           conv3d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 72, 72, 144,  0           dropout_6[0][0]                  \n",
      "                                                                 concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 72, 72, 144,  288         concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 72, 72, 144,  0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_9 (Conv3D)               (None, 72, 72, 144,  62224       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 72, 72, 144,  0           conv3d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 72, 72, 144,  0           dropout_7[0][0]                  \n",
      "                                                                 concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 72, 72, 144,  288         concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 72, 72, 144,  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_10 (Conv3D)              (None, 72, 72, 144,  25760       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 72, 72, 144,  0           conv3d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_11 (Conv3D)              (None, 36, 36, 144,  25760       dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 36, 36, 144,  144         conv3d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 36, 36, 144,  0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_12 (Conv3D)              (None, 36, 36, 144,  69136       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 36, 36, 144,  0           conv3d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 36, 36, 144,  0           dropout_9[0][0]                  \n",
      "                                                                 conv3d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 36, 36, 144,  144         concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 36, 36, 144,  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_13 (Conv3D)              (None, 36, 36, 144,  76048       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 36, 36, 144,  0           conv3d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 36, 36, 144,  0           dropout_10[0][0]                 \n",
      "                                                                 concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 36, 36, 144,  144         concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 36, 36, 144,  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_14 (Conv3D)              (None, 36, 36, 144,  82960       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_11 (Dropout)            (None, 36, 36, 144,  0           conv3d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 36, 36, 144,  0           dropout_11[0][0]                 \n",
      "                                                                 concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 36, 36, 144,  144         concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 36, 36, 144,  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_15 (Conv3D)              (None, 36, 36, 144,  89872       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_12 (Dropout)            (None, 36, 36, 144,  0           conv3d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 36, 36, 144,  0           dropout_12[0][0]                 \n",
      "                                                                 concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 36, 36, 144,  144         concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 36, 36, 144,  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_16 (Conv3D)              (None, 36, 36, 144,  96784       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 36, 36, 144,  0           conv3d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_11 (Concatenate)    (None, 36, 36, 144,  0           dropout_13[0][0]                 \n",
      "                                                                 concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 36, 36, 144,  144         concatenate_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 36, 36, 144,  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_17 (Conv3D)              (None, 36, 36, 144,  57840       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_14 (Dropout)            (None, 36, 36, 144,  0           conv3d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_18 (Conv3D)              (None, 18, 18, 144,  57840       dropout_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 18, 18, 144,  72          conv3d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 18, 18, 144,  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_19 (Conv3D)              (None, 18, 18, 144,  103696      activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_15 (Dropout)            (None, 18, 18, 144,  0           conv3d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_12 (Concatenate)    (None, 18, 18, 144,  0           dropout_15[0][0]                 \n",
      "                                                                 conv3d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 18, 18, 144,  72          concatenate_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 18, 18, 144,  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_20 (Conv3D)              (None, 18, 18, 144,  110608      activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_16 (Dropout)            (None, 18, 18, 144,  0           conv3d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_13 (Concatenate)    (None, 18, 18, 144,  0           dropout_16[0][0]                 \n",
      "                                                                 concatenate_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 18, 18, 144,  72          concatenate_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 18, 18, 144,  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_21 (Conv3D)              (None, 18, 18, 144,  117520      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_17 (Dropout)            (None, 18, 18, 144,  0           conv3d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_14 (Concatenate)    (None, 18, 18, 144,  0           dropout_17[0][0]                 \n",
      "                                                                 concatenate_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 18, 18, 144,  72          concatenate_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 18, 18, 144,  0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_22 (Conv3D)              (None, 18, 18, 144,  124432      activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_18 (Dropout)            (None, 18, 18, 144,  0           conv3d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_15 (Concatenate)    (None, 18, 18, 144,  0           dropout_18[0][0]                 \n",
      "                                                                 concatenate_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 18, 18, 144,  72          concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 18, 18, 144,  0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_23 (Conv3D)              (None, 18, 18, 144,  131344      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_19 (Dropout)            (None, 18, 18, 144,  0           conv3d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_16 (Concatenate)    (None, 18, 18, 144,  0           dropout_19[0][0]                 \n",
      "                                                                 concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 18, 18, 144,  72          concatenate_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 18, 18, 144,  0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_24 (Conv3D)              (None, 18, 18, 144,  138256      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_20 (Dropout)            (None, 18, 18, 144,  0           conv3d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_17 (Concatenate)    (None, 18, 18, 144,  0           dropout_20[0][0]                 \n",
      "                                                                 concatenate_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 18, 18, 144,  72          concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 18, 18, 144,  0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_25 (Conv3D)              (None, 18, 18, 144,  113232      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_21 (Dropout)            (None, 18, 18, 144,  0           conv3d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_26 (Conv3D)              (None, 9, 9, 144, 33 113232      dropout_21[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 9, 9, 144, 33 36          conv3d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 9, 9, 144, 33 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_27 (Conv3D)              (None, 9, 9, 144, 16 145168      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_22 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_18 (Concatenate)    (None, 9, 9, 144, 35 0           dropout_22[0][0]                 \n",
      "                                                                 conv3d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 9, 9, 144, 35 36          concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 9, 9, 144, 35 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_28 (Conv3D)              (None, 9, 9, 144, 16 152080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_23 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_19 (Concatenate)    (None, 9, 9, 144, 36 0           dropout_23[0][0]                 \n",
      "                                                                 concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 9, 9, 144, 36 36          concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 9, 9, 144, 36 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_29 (Conv3D)              (None, 9, 9, 144, 16 158992      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_24 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_20 (Concatenate)    (None, 9, 9, 144, 38 0           dropout_24[0][0]                 \n",
      "                                                                 concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 9, 9, 144, 38 36          concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 9, 9, 144, 38 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_30 (Conv3D)              (None, 9, 9, 144, 16 165904      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_25 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_21 (Concatenate)    (None, 9, 9, 144, 40 0           dropout_25[0][0]                 \n",
      "                                                                 concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 9, 9, 144, 40 36          concatenate_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 9, 9, 144, 40 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_31 (Conv3D)              (None, 9, 9, 144, 16 172816      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_26 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_22 (Concatenate)    (None, 9, 9, 144, 41 0           dropout_26[0][0]                 \n",
      "                                                                 concatenate_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 9, 9, 144, 41 36          concatenate_22[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 9, 9, 144, 41 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_32 (Conv3D)              (None, 9, 9, 144, 16 179728      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_27 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_23 (Concatenate)    (None, 9, 9, 144, 43 0           dropout_27[0][0]                 \n",
      "                                                                 concatenate_22[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 9, 9, 144, 43 36          concatenate_23[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 9, 9, 144, 43 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_33 (Conv3D)              (None, 9, 9, 144, 16 186640      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_28 (Dropout)            (None, 9, 9, 144, 16 0           conv3d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_24 (Concatenate)    (None, 9, 9, 144, 44 0           dropout_28[0][0]                 \n",
      "                                                                 concatenate_23[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose (Conv3DTranspo (None, 18, 18, 144,  1354864     concatenate_24[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_25 (Concatenate)    (None, 18, 18, 144,  0           conv3d_transpose[0][0]           \n",
      "                                                                 concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 18, 18, 144,  72          concatenate_25[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 18, 18, 144,  0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_34 (Conv3D)              (None, 18, 18, 144,  193552      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_29 (Dropout)            (None, 18, 18, 144,  0           conv3d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_26 (Concatenate)    (None, 18, 18, 144,  0           dropout_29[0][0]                 \n",
      "                                                                 concatenate_25[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 18, 18, 144,  72          concatenate_26[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 18, 18, 144,  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_35 (Conv3D)              (None, 18, 18, 144,  200464      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_30 (Dropout)            (None, 18, 18, 144,  0           conv3d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_27 (Concatenate)    (None, 18, 18, 144,  0           dropout_30[0][0]                 \n",
      "                                                                 concatenate_26[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 18, 18, 144,  72          concatenate_27[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 18, 18, 144,  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_36 (Conv3D)              (None, 18, 18, 144,  207376      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_31 (Dropout)            (None, 18, 18, 144,  0           conv3d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_28 (Concatenate)    (None, 18, 18, 144,  0           dropout_31[0][0]                 \n",
      "                                                                 concatenate_27[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 18, 18, 144,  72          concatenate_28[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 18, 18, 144,  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_37 (Conv3D)              (None, 18, 18, 144,  214288      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_32 (Dropout)            (None, 18, 18, 144,  0           conv3d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_29 (Concatenate)    (None, 18, 18, 144,  0           dropout_32[0][0]                 \n",
      "                                                                 concatenate_28[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 18, 18, 144,  72          concatenate_29[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 18, 18, 144,  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_38 (Conv3D)              (None, 18, 18, 144,  221200      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_33 (Dropout)            (None, 18, 18, 144,  0           conv3d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_30 (Concatenate)    (None, 18, 18, 144,  0           dropout_33[0][0]                 \n",
      "                                                                 concatenate_29[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 18, 18, 144,  72          concatenate_30[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 18, 18, 144,  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_39 (Conv3D)              (None, 18, 18, 144,  228112      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_34 (Dropout)            (None, 18, 18, 144,  0           conv3d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_31 (Concatenate)    (None, 18, 18, 144,  0           dropout_34[0][0]                 \n",
      "                                                                 concatenate_30[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_1 (Conv3DTrans (None, 36, 36, 144,  1410144     concatenate_31[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_32 (Concatenate)    (None, 36, 36, 144,  0           conv3d_transpose_1[0][0]         \n",
      "                                                                 concatenate_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 36, 36, 144,  144         concatenate_32[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 36, 36, 144,  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_40 (Conv3D)              (None, 36, 36, 144,  145168      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_35 (Dropout)            (None, 36, 36, 144,  0           conv3d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_33 (Concatenate)    (None, 36, 36, 144,  0           dropout_35[0][0]                 \n",
      "                                                                 concatenate_32[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 36, 36, 144,  144         concatenate_33[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 36, 36, 144,  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_41 (Conv3D)              (None, 36, 36, 144,  152080      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_36 (Dropout)            (None, 36, 36, 144,  0           conv3d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_34 (Concatenate)    (None, 36, 36, 144,  0           dropout_36[0][0]                 \n",
      "                                                                 concatenate_33[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 36, 36, 144,  144         concatenate_34[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 36, 36, 144,  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_42 (Conv3D)              (None, 36, 36, 144,  158992      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_37 (Dropout)            (None, 36, 36, 144,  0           conv3d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_35 (Concatenate)    (None, 36, 36, 144,  0           dropout_37[0][0]                 \n",
      "                                                                 concatenate_34[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 36, 36, 144,  144         concatenate_35[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 36, 36, 144,  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_43 (Conv3D)              (None, 36, 36, 144,  165904      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_38 (Dropout)            (None, 36, 36, 144,  0           conv3d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_36 (Concatenate)    (None, 36, 36, 144,  0           dropout_38[0][0]                 \n",
      "                                                                 concatenate_35[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 36, 36, 144,  144         concatenate_36[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 36, 36, 144,  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_44 (Conv3D)              (None, 36, 36, 144,  172816      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_39 (Dropout)            (None, 36, 36, 144,  0           conv3d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_37 (Concatenate)    (None, 36, 36, 144,  0           dropout_39[0][0]                 \n",
      "                                                                 concatenate_36[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_2 (Conv3DTrans (None, 72, 72, 144,  898640      concatenate_37[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_38 (Concatenate)    (None, 72, 72, 144,  0           conv3d_transpose_2[0][0]         \n",
      "                                                                 concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 72, 72, 144,  288         concatenate_38[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 72, 72, 144,  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_45 (Conv3D)              (None, 72, 72, 144,  103696      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_40 (Dropout)            (None, 72, 72, 144,  0           conv3d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_39 (Concatenate)    (None, 72, 72, 144,  0           dropout_40[0][0]                 \n",
      "                                                                 concatenate_38[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 72, 72, 144,  288         concatenate_39[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 72, 72, 144,  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_46 (Conv3D)              (None, 72, 72, 144,  110608      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_41 (Dropout)            (None, 72, 72, 144,  0           conv3d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_40 (Concatenate)    (None, 72, 72, 144,  0           dropout_41[0][0]                 \n",
      "                                                                 concatenate_39[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 72, 72, 144,  288         concatenate_40[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 72, 72, 144,  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_47 (Conv3D)              (None, 72, 72, 144,  117520      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_42 (Dropout)            (None, 72, 72, 144,  0           conv3d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_41 (Concatenate)    (None, 72, 72, 144,  0           dropout_42[0][0]                 \n",
      "                                                                 concatenate_40[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 72, 72, 144,  288         concatenate_41[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 72, 72, 144,  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_48 (Conv3D)              (None, 72, 72, 144,  124432      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_43 (Dropout)            (None, 72, 72, 144,  0           conv3d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_42 (Concatenate)    (None, 72, 72, 144,  0           dropout_43[0][0]                 \n",
      "                                                                 concatenate_41[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_3 (Conv3DTrans (None, 144, 144, 144 525376      concatenate_42[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_43 (Concatenate)    (None, 144, 144, 144 0           conv3d_transpose_3[0][0]         \n",
      "                                                                 concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 144, 144, 144 576         concatenate_43[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 144, 144, 144 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_49 (Conv3D)              (None, 144, 144, 144 69136       activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_44 (Dropout)            (None, 144, 144, 144 0           conv3d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_44 (Concatenate)    (None, 144, 144, 144 0           dropout_44[0][0]                 \n",
      "                                                                 concatenate_43[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 144, 144, 144 576         concatenate_44[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 144, 144, 144 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_50 (Conv3D)              (None, 144, 144, 144 76048       activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_45 (Dropout)            (None, 144, 144, 144 0           conv3d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_45 (Concatenate)    (None, 144, 144, 144 0           dropout_45[0][0]                 \n",
      "                                                                 concatenate_44[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 144, 144, 144 576         concatenate_45[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 144, 144, 144 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_51 (Conv3D)              (None, 144, 144, 144 82960       activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_46 (Dropout)            (None, 144, 144, 144 0           conv3d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_46 (Concatenate)    (None, 144, 144, 144 0           dropout_46[0][0]                 \n",
      "                                                                 concatenate_45[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_52 (Conv3D)              (None, 144, 144, 144 2508        concatenate_46[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 144, 144, 144 0           conv3d_52[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 9,952,608\n",
      "Trainable params: 9,947,910\n",
      "Non-trainable params: 4,698\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "\n",
    "from tensorflow.keras import Model, Input\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, add, multiply\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, MaxPooling3D\n",
    "from tensorflow.keras.layers import Conv3D, Conv3DTranspose\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Activation, Dropout\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.regularizers import l2\n",
    "\n",
    "\n",
    "def denseBlock(t, nb_layers):\n",
    "    for _ in range(nb_layers):\n",
    "        tmp = t\n",
    "        t = BatchNormalization(axis=1,\n",
    "                                gamma_regularizer=l2(0.0001),\n",
    "                                beta_regularizer=l2(0.0001))(t)\n",
    "\n",
    "        t = Activation('relu')(t)\n",
    "        t = Conv3D(16, kernel_size=(3, 3, 3), padding='same', kernel_initializer='he_uniform', data_format='channels_last')(t)\n",
    "        t = Dropout(0.2)(t)\n",
    "        t = concatenate([t, tmp])\n",
    "    return t\n",
    "\n",
    "def transitionDown(t, nb_features):\n",
    "    t = BatchNormalization(axis=1,\n",
    "                            gamma_regularizer=l2(0.0001),\n",
    "                            beta_regularizer=l2(0.0001))(t)\n",
    "    t = Activation('relu')(t)\n",
    "    t = Conv3D(nb_features, kernel_size=(1, 1, 1), padding='same', kernel_initializer='he_uniform', data_format='channels_last')(t)\n",
    "    t = Dropout(0.2)(t)\n",
    "    #t = MaxPooling3D(pool_size=(2, 2, 2), strides=2, padding='same', data_format='channels_last')(t)\n",
    "    t = Conv3D(nb_features, kernel_size=(1, 1, 1),strides=(2,2,1), padding='same', data_format='channels_last')(t)\n",
    "    \n",
    "    return t\n",
    "\n",
    "def Tiramisu3D(layer_per_block, n_pool=4, growth_rate=16):\n",
    "    input_layer = Input(shape=INPUT_SHAPE)\n",
    "    t = Conv3D(48, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same')(input_layer)\n",
    "\n",
    "    #dense block\n",
    "    nb_features = 48\n",
    "    skip_connections = []\n",
    "    for i in range(n_pool):\n",
    "        t = denseBlock(t, layer_per_block[i])\n",
    "        skip_connections.append(t)\n",
    "        nb_features += growth_rate * layer_per_block[i]\n",
    "        t = transitionDown(t, nb_features)\n",
    "\n",
    "    t = denseBlock(t, layer_per_block[n_pool]) # bottle neck\n",
    "\n",
    "    skip_connections = skip_connections[::-1] #subvert the array\n",
    "\n",
    "    for i in range(n_pool):\n",
    "        keep_nb_features = growth_rate * layer_per_block[n_pool + i]\n",
    "        t = Conv3DTranspose(keep_nb_features, strides=(2,2,1), kernel_size=(3, 3, 3), padding='same', data_format='channels_last')(t) # transition Up\n",
    "        t = concatenate([t, skip_connections[i]])\n",
    "\n",
    "        t = denseBlock(t, layer_per_block[n_pool+i+1])\n",
    "\n",
    "    t = Conv3D(12, kernel_size=(1, 1, 1), padding='same', kernel_initializer='he_uniform', data_format='channels_last')(t)\n",
    "    output_layer = Activation('softmax')(t)\n",
    "    return Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "layer_per_block =  [3, 4, 5, 6, 7, 6, 5, 4, 3]\n",
    "#layer_per_block = [4, 5, 7, 10, 12, 15, 12, 10, 7, 5, 4]\n",
    "tiramisu = Tiramisu3D(layer_per_block)\n",
    "\n",
    "tiramisu.summary()\n",
    "#plot_model(model, to_file='model.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attention gate input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "\n",
    "from tensorflow.keras import Model, Input\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, add, multiply\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, MaxPooling3D\n",
    "from tensorflow.keras.layers import Conv3D, Conv3DTranspose\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Activation, Dropout\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.regularizers import l2\n",
    "\n",
    "PROJECT_NAME = 'hector_attention_gate'\n",
    "project_name = PROJECT_NAME\n",
    "\n",
    "def attention_block_3d(x, g, inter_channel, data_format='channels_last'):\n",
    "    # theta_x(?,g_height,g_width,inter_channel)\n",
    "\n",
    "    theta_x = Conv3D(inter_channel, [1, 1, 1], strides=[1, 1, 1], data_format=data_format)(x)\n",
    "\n",
    "    # phi_g(?,g_height,g_width,inter_channel)\n",
    "\n",
    "    phi_g = Conv3D(inter_channel, [1, 1, 1], strides=[1, 1, 1], data_format=data_format)(g)\n",
    "\n",
    "    # f(?,g_height,g_width,inter_channel)\n",
    "\n",
    "    f = Activation('relu')(add([theta_x, phi_g]))\n",
    "\n",
    "    # psi_f(?,g_height,g_width,1)\n",
    "\n",
    "    psi_f = Conv3D(1, [1, 1, 1], strides=[1, 1, 1], data_format=data_format)(f)\n",
    "\n",
    "    rate = Activation('sigmoid')(psi_f)\n",
    "\n",
    "    # rate(?,x_height,x_width)\n",
    "\n",
    "    # att_x(?,x_height,x_width,x_channel)\n",
    "\n",
    "    att_x = multiply([x, rate])\n",
    "\n",
    "    return att_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_46\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_47 (InputLayer)           [(None, 144, 144, 14 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_908 (Conv3D)             (None, 144, 144, 144 1760        input_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1037 (Concatenate)  (None, 144, 144, 144 0           input_47[0][0]                   \n",
      "                                                                 conv3d_908[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_909 (Conv3D)             (None, 144, 144, 144 29408       concatenate_1037[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1038 (Concatenate)  (None, 144, 144, 144 0           input_47[0][0]                   \n",
      "                                                                 conv3d_909[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_180 (MaxPooling3D (None, 72, 72, 72, 3 0           concatenate_1038[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_910 (Conv3D)             (None, 72, 72, 72, 6 58816       max_pooling3d_180[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1039 (Concatenate)  (None, 72, 72, 72, 9 0           max_pooling3d_180[0][0]          \n",
      "                                                                 conv3d_910[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_911 (Conv3D)             (None, 72, 72, 72, 6 169408      concatenate_1039[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1040 (Concatenate)  (None, 72, 72, 72, 9 0           max_pooling3d_180[0][0]          \n",
      "                                                                 conv3d_911[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_181 (MaxPooling3D (None, 36, 36, 36, 9 0           concatenate_1040[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_912 (Conv3D)             (None, 36, 36, 36, 1 338816      max_pooling3d_181[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1041 (Concatenate)  (None, 36, 36, 36, 2 0           max_pooling3d_181[0][0]          \n",
      "                                                                 conv3d_912[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_913 (Conv3D)             (None, 36, 36, 36, 1 781184      concatenate_1041[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1042 (Concatenate)  (None, 36, 36, 36, 2 0           max_pooling3d_181[0][0]          \n",
      "                                                                 conv3d_913[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_182 (MaxPooling3D (None, 18, 18, 18, 2 0           concatenate_1042[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_914 (Conv3D)             (None, 18, 18, 18, 2 1562368     max_pooling3d_182[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1043 (Concatenate)  (None, 18, 18, 18, 4 0           max_pooling3d_182[0][0]          \n",
      "                                                                 conv3d_914[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_915 (Conv3D)             (None, 18, 18, 18, 2 3331840     concatenate_1043[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1044 (Concatenate)  (None, 18, 18, 18, 4 0           max_pooling3d_182[0][0]          \n",
      "                                                                 conv3d_915[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_183 (MaxPooling3D (None, 9, 9, 9, 482) 0           concatenate_1044[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_92 (Dropout)            (None, 9, 9, 9, 482) 0           max_pooling3d_183[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_916 (Conv3D)             (None, 9, 9, 9, 512) 6663680     dropout_92[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1045 (Concatenate)  (None, 9, 9, 9, 994) 0           max_pooling3d_183[0][0]          \n",
      "                                                                 conv3d_916[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_917 (Conv3D)             (None, 9, 9, 9, 512) 13741568    concatenate_1045[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1046 (Concatenate)  (None, 9, 9, 9, 994) 0           max_pooling3d_183[0][0]          \n",
      "                                                                 conv3d_917[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_184 (Conv3DTra (None, 18, 18, 18, 2 2035968     concatenate_1046[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1047 (Concatenate)  (None, 18, 18, 18, 7 0           conv3d_transpose_184[0][0]       \n",
      "                                                                 concatenate_1044[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_918 (Conv3D)             (None, 18, 18, 18, 2 5101312     concatenate_1047[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1048 (Concatenate)  (None, 18, 18, 18, 9 0           concatenate_1047[0][0]           \n",
      "                                                                 conv3d_918[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_919 (Conv3D)             (None, 18, 18, 18, 2 6870784     concatenate_1048[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1049 (Concatenate)  (None, 18, 18, 18, 9 0           concatenate_1047[0][0]           \n",
      "                                                                 conv3d_919[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_185 (Conv3DTra (None, 36, 36, 36, 1 1017984     concatenate_1049[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1050 (Concatenate)  (None, 36, 36, 36, 2 0           conv3d_transpose_185[0][0]       \n",
      "                                                                 conv3d_913[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_920 (Conv3D)             (None, 36, 36, 36, 1 884864      concatenate_1050[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1051 (Concatenate)  (None, 36, 36, 36, 3 0           concatenate_1050[0][0]           \n",
      "                                                                 conv3d_920[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_921 (Conv3D)             (None, 36, 36, 36, 1 1327232     concatenate_1051[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1052 (Concatenate)  (None, 36, 36, 36, 3 0           concatenate_1050[0][0]           \n",
      "                                                                 conv3d_921[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_186 (Conv3DTra (None, 72, 72, 72, 6 196672      concatenate_1052[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1053 (Concatenate)  (None, 72, 72, 72, 1 0           conv3d_transpose_186[0][0]       \n",
      "                                                                 conv3d_911[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_922 (Conv3D)             (None, 72, 72, 72, 6 221248      concatenate_1053[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1054 (Concatenate)  (None, 72, 72, 72, 1 0           concatenate_1053[0][0]           \n",
      "                                                                 conv3d_922[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_923 (Conv3D)             (None, 72, 72, 72, 6 331840      concatenate_1054[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1055 (Concatenate)  (None, 72, 72, 72, 1 0           concatenate_1053[0][0]           \n",
      "                                                                 conv3d_923[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_187 (Conv3DTra (None, 144, 144, 144 49184       concatenate_1055[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1056 (Concatenate)  (None, 144, 144, 144 0           conv3d_transpose_187[0][0]       \n",
      "                                                                 conv3d_909[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_924 (Conv3D)             (None, 144, 144, 144 55328       concatenate_1056[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1057 (Concatenate)  (None, 144, 144, 144 0           concatenate_1056[0][0]           \n",
      "                                                                 conv3d_924[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_925 (Conv3D)             (None, 144, 144, 144 82976       concatenate_1057[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1058 (Concatenate)  (None, 144, 144, 144 0           concatenate_1056[0][0]           \n",
      "                                                                 conv3d_925[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_926 (Conv3D)             (None, 144, 144, 144 97          concatenate_1058[0][0]           \n",
      "==================================================================================================\n",
      "Total params: 44,854,337\n",
      "Trainable params: 44,854,337\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def att_input_unet(att_input= True, inter_channel = 1):\n",
    "    inputs = Input(INPUT_SHAPE)\n",
    "    if att_input:\n",
    "        att = attention_block_3d(inputs[...,0,np.newaxis], inputs[...,1, np.newaxis], inter_channel)\n",
    "        conv11 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(att)\n",
    "    else:\n",
    "        conv11 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(inputs)\n",
    "        \n",
    "    conc11 = concatenate([inputs, conv11], axis=4)\n",
    "    conv12 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conc11)\n",
    "    conc12 = concatenate([inputs, conv12], axis=4)\n",
    "    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conc12)\n",
    "\n",
    "    conv21 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(pool1)\n",
    "    conc21 = concatenate([pool1, conv21], axis=4)\n",
    "    conv22 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conc21)\n",
    "    conc22 = concatenate([pool1, conv22], axis=4)\n",
    "    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conc22)\n",
    "\n",
    "    conv31 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(pool2)\n",
    "    conc31 = concatenate([pool2, conv31], axis=4)\n",
    "    conv32 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conc31)\n",
    "    conc32 = concatenate([pool2, conv32], axis=4)\n",
    "    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conc32)\n",
    "\n",
    "    conv41 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(pool3)\n",
    "    conc41 = concatenate([pool3, conv41], axis=4)\n",
    "    conv42 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conc41)\n",
    "    conc42 = concatenate([pool3, conv42], axis=4)\n",
    "    pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conc42)\n",
    "    \n",
    "    dropout1 = Dropout(0.5)(pool4)\n",
    "    conv51 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(dropout1)\n",
    "    conc51 = concatenate([pool4, conv51], axis=4)\n",
    "    conv52 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conc51)\n",
    "    conc52 = concatenate([pool4, conv52], axis=4)\n",
    "\n",
    "    up6 = concatenate([Conv3DTranspose(256, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc52), conc42], axis=4)\n",
    "    conv61 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(up6)\n",
    "    conc61 = concatenate([up6, conv61], axis=4)\n",
    "    conv62 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conc61)\n",
    "    conc62 = concatenate([up6, conv62], axis=4)\n",
    "\n",
    "    up7 = concatenate([Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc62), conv32], axis=4)\n",
    "    conv71 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(up7)\n",
    "    conc71 = concatenate([up7, conv71], axis=4)\n",
    "    conv72 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conc71)\n",
    "    conc72 = concatenate([up7, conv72], axis=4)\n",
    "\n",
    "    up8 = concatenate([Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc72), conv22], axis=4)\n",
    "    conv81 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(up8)\n",
    "    conc81 = concatenate([up8, conv81], axis=4)\n",
    "    conv82 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conc81)\n",
    "    conc82 = concatenate([up8, conv82], axis=4)\n",
    "\n",
    "    up9 = concatenate([Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc82), conv12], axis=4)\n",
    "    conv91 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(up9)\n",
    "    conc91 = concatenate([up9, conv91], axis=4)\n",
    "    conv92 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conc91)\n",
    "    conc92 = concatenate([up9, conv92], axis=4)\n",
    "\n",
    "    conv10 = Conv3D(1, (1, 1, 1), activation='sigmoid')(conc92)\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[conv10])\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    return model\n",
    "\n",
    "model = att_input_unet(att_input= False, inter_channel = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# learning rate finder, learning rate restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementation\n",
    "#Both finding the optimal range of learning rates and assigning a learning rate schedule can be implemented quite trivially using Keras Callbacks.\n",
    "#Finding the optimal learning rate range\n",
    "#We can write a Keras Callback which tracks the loss associated with a learning rate varied linearly over a defined range.\n",
    "from tensorflow.keras.callbacks import Callback\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class LRFinder(Callback):\n",
    "    \n",
    "    '''\n",
    "    A simple callback for finding the optimal learning rate range for your model + dataset. \n",
    "    \n",
    "    # Usage\n",
    "        ```python\n",
    "            lr_finder = LRFinder(min_lr=1e-5, \n",
    "                                 max_lr=1e-2, \n",
    "                                 steps_per_epoch=np.ceil(epoch_size/batch_size), \n",
    "                                 epochs=3)\n",
    "            model.fit(X_train, Y_train, callbacks=[lr_finder])\n",
    "            \n",
    "            lr_finder.plot_loss()\n",
    "        ```\n",
    "    \n",
    "    # Arguments\n",
    "        min_lr: The lower bound of the learning rate range for the experiment.\n",
    "        max_lr: The upper bound of the learning rate range for the experiment.\n",
    "        steps_per_epoch: Number of mini-batches in the dataset. Calculated as `np.ceil(epoch_size/batch_size)`. \n",
    "        epochs: Number of epochs to run experiment. Usually between 2 and 4 epochs is sufficient. \n",
    "        \n",
    "    # References\n",
    "        Blog post: jeremyjordan.me/nn-learning-rate\n",
    "        Original paper: https://arxiv.org/abs/1506.01186\n",
    "    '''\n",
    "\n",
    "    def __init__(self, min_lr=1e-5, max_lr=1e-2, steps_per_epoch=None, epochs=None):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.min_lr = min_lr\n",
    "        self.max_lr = max_lr\n",
    "        self.total_iterations = steps_per_epoch * epochs\n",
    "        self.iteration = 0\n",
    "        self.history = {}\n",
    "        \n",
    "    def clr(self):\n",
    "        '''Calculate the learning rate.'''\n",
    "        x = self.iteration / self.total_iterations \n",
    "        return self.min_lr + (self.max_lr-self.min_lr) * x\n",
    "        \n",
    "    def on_train_begin(self, logs=None):\n",
    "        '''Initialize the learning rate to the minimum value at the start of training.'''\n",
    "        logs = logs or {}\n",
    "        K.set_value(self.model.optimizer.lr, self.min_lr)\n",
    "        \n",
    "    def on_batch_end(self, epoch, logs=None):\n",
    "        '''Record previous batch statistics and update the learning rate.'''\n",
    "        logs = logs or {}\n",
    "        self.iteration += 1\n",
    "\n",
    "        self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))\n",
    "        self.history.setdefault('iterations', []).append(self.iteration)\n",
    "\n",
    "        for k, v in logs.items():\n",
    "            self.history.setdefault(k, []).append(v)\n",
    "            \n",
    "        K.set_value(self.model.optimizer.lr, self.clr())\n",
    "    \n",
    "    def plot_lr(self):\n",
    "        '''Helper function to quickly inspect the learning rate schedule.'''\n",
    "        plt.plot(self.history['iterations'], self.history['lr'])\n",
    "        plt.yscale('log')\n",
    "        plt.xlabel('Iteration')\n",
    "        plt.ylabel('Learning rate')\n",
    "        \n",
    "    def plot_loss(self):\n",
    "        '''Helper function to quickly observe the learning rate experiment results.'''\n",
    "        plt.plot(self.history['lr'], self.history['loss'])\n",
    "        plt.xscale('log')\n",
    "        plt.xlabel('Learning rate')\n",
    "        plt.ylabel('Loss')\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "class SGDRScheduler(Callback):\n",
    "    '''Cosine annealing learning rate scheduler with periodic restarts.\n",
    "    # Usage\n",
    "        ```python\n",
    "            schedule = SGDRScheduler(min_lr=1e-5,\n",
    "                                     max_lr=1e-2,\n",
    "                                     steps_per_epoch=np.ceil(epoch_size/batch_size),\n",
    "                                     lr_decay=0.9,\n",
    "                                     cycle_length=5,\n",
    "                                     mult_factor=1.5)\n",
    "            model.fit(X_train, Y_train, epochs=100, callbacks=[schedule])\n",
    "        ```\n",
    "    # Arguments\n",
    "        min_lr: The lower bound of the learning rate range for the experiment.\n",
    "        max_lr: The upper bound of the learning rate range for the experiment.\n",
    "        steps_per_epoch: Number of mini-batches in the dataset. Calculated as `np.ceil(epoch_size/batch_size)`. \n",
    "        lr_decay: Reduce the max_lr after the completion of each cycle.\n",
    "                  Ex. To reduce the max_lr by 20% after each cycle, set this value to 0.8.\n",
    "        cycle_length: Initial number of epochs in a cycle.\n",
    "        mult_factor: Scale epochs_to_restart after each full cycle completion.\n",
    "    # References\n",
    "        Blog post: jeremyjordan.me/nn-learning-rate\n",
    "        Original paper: http://arxiv.org/abs/1608.03983\n",
    "    '''\n",
    "    def __init__(self,\n",
    "                 min_lr,\n",
    "                 max_lr,\n",
    "                 steps_per_epoch,\n",
    "                 lr_decay=1,\n",
    "                 cycle_length=10,\n",
    "                 mult_factor=2):\n",
    "\n",
    "        self.min_lr = min_lr\n",
    "        self.max_lr = max_lr\n",
    "        self.lr_decay = lr_decay\n",
    "\n",
    "        self.batch_since_restart = 0\n",
    "        self.next_restart = cycle_length\n",
    "\n",
    "        self.steps_per_epoch = steps_per_epoch\n",
    "\n",
    "        self.cycle_length = cycle_length\n",
    "        self.mult_factor = mult_factor\n",
    "\n",
    "        self.history = {}\n",
    "\n",
    "    def clr(self):\n",
    "        '''Calculate the learning rate.'''\n",
    "        fraction_to_restart = self.batch_since_restart / (self.steps_per_epoch * self.cycle_length)\n",
    "        lr = self.min_lr + 0.5 * (self.max_lr - self.min_lr) * (1 + np.cos(fraction_to_restart * np.pi))\n",
    "        return lr\n",
    "\n",
    "    def on_train_begin(self, logs={}):\n",
    "        '''Initialize the learning rate to the minimum value at the start of training.'''\n",
    "        logs = logs or {}\n",
    "        K.set_value(self.model.optimizer.lr, self.max_lr)\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        '''Record previous batch statistics and update the learning rate.'''\n",
    "        logs = logs or {}\n",
    "        self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))\n",
    "        for k, v in logs.items():\n",
    "            self.history.setdefault(k, []).append(v)\n",
    "\n",
    "        self.batch_since_restart += 1\n",
    "        K.set_value(self.model.optimizer.lr, self.clr())\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        '''Check for end of current cycle, apply restarts when necessary.'''\n",
    "        if epoch + 1 == self.next_restart:\n",
    "            self.batch_since_restart = 0\n",
    "            self.cycle_length = np.ceil(self.cycle_length * self.mult_factor)\n",
    "            self.next_restart += self.cycle_length\n",
    "            self.max_lr *= self.lr_decay\n",
    "            self.best_weights = self.model.get_weights()\n",
    "\n",
    "    def on_train_end(self, logs={}):\n",
    "        '''Set weights to the values from the end of the most recent cycle for best performance.'''\n",
    "        self.model.set_weights(self.best_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau\n",
    "from livelossplot import PlotLossesKerasTF\n",
    "from lovaz1 import lovasz_hinge \n",
    "\n",
    "project_name = PROJECT_NAME\n",
    "\n",
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection +K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())\n",
    "\n",
    "\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return 1-dice_coef(y_true, y_pred)\n",
    "\n",
    "def categorical_focal_loss_fixed(logits, labels, gamma=2., alpha=.25):\n",
    "    \"\"\"\n",
    "    :param logits: A tensor resulting from a softmax\n",
    "    :param labels: A tensor of the same shape as `logits`\n",
    "    :input shape: [batch, depth, hight, width, channels]\n",
    "    :return: Output tensor.\n",
    "    \"\"\"\n",
    "\n",
    "    # Scale predictions so that the class probas of each sample sum to 1\n",
    "    logits /= K.sum(logits, axis=-1, keepdims=True)\n",
    "\n",
    "    # Clip the prediction value to prevent NaN's and Inf's\n",
    "    epsilon = K.epsilon()\n",
    "    logits = K.clip(logits, epsilon, 1. - epsilon)\n",
    "\n",
    "    # Calculate Cross Entropy\n",
    "    cross_entropy = -labels * K.log(logits)\n",
    "\n",
    "    # Calculate Focal Loss\n",
    "    loss = alpha * K.pow(1 - logits, gamma) * cross_entropy\n",
    "\n",
    "    # Sum the losses in mini_batch\n",
    "#    return K.sum(loss, axis=1)\n",
    "    return  K.mean(loss)\n",
    "\n",
    "\n",
    "def hyper_loss(y_true, y_pred):\n",
    "    return  dice_coef_loss(y_true, y_pred) + categorical_focal_loss_fixed(y_true, y_pred)\n",
    "\n",
    "\n",
    "\n",
    "def bce_dice_loss(y_true, y_pred):\n",
    "    return 0.5 * tf.keras.losses.binary_crossentropy(y_true, y_pred) + dice_coef_loss(y_true, y_pred)\n",
    "\n",
    "def lovaz_loss(y_true, y_pred):\n",
    "    y_true, y_pred = K.cast(y_true, 'int32'), K.cast(y_pred, 'float32')\n",
    "    #logits = K.log(y_pred / (1. - y_pred))\n",
    "    \n",
    "    logits = y_pred \n",
    "    loss = lovasz_hinge(logits, y_true, per_image = False, ignore = None)\n",
    "    return loss\n",
    "\n",
    "def lovaz_bce_loss(y_true, y_pred):\n",
    "    return 0.5*tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_true, y_pred) + lovaz_loss(y_true, y_pred))\n",
    "\n",
    "\n",
    "def dice_lovaz(y_true, y_pred):\n",
    "    return 0.5*lovaz_loss(y_true, y_pred) + dice_coef_loss(y_true, y_pred)\n",
    "\n",
    "def train(att_input= True, inter_channel = 1, loss_function= dice_coef_loss, retrain = False, L2 = False):\n",
    "    batch_size = BATCH_SIZE\n",
    "    project_name = PROJECT_NAME\n",
    "    print(project_name)\n",
    "    print('-'*30)\n",
    "    print('Creating and compiling model...')\n",
    "    print('-'*30)\n",
    "    model = att_input_unet(att_input= att_input, inter_channel = inter_channel)\n",
    "    weight_dir = 'weights'\n",
    "    if not os.path.exists(weight_dir):\n",
    "        os.mkdir(weight_dir)\n",
    "        \n",
    "    if retrain != False:\n",
    "        # It can be used to reconstruct the model identically.\n",
    "        model = tf.keras.models.load_model(os.path.join(weight_dir,retrain+'.h5'), \n",
    "                                           custom_objects={'hyper_loss':hyper_loss,\n",
    "                                                           'bce_dice_loss': bce_dice_loss,\n",
    "                                                           'dice_coef': dice_coef,\n",
    "                                                           'lovaz_loss':lovaz_loss,\n",
    "                                                           'dice_lovaz':dice_lovaz,\n",
    "                                                           'lovaz_bce_loss': lovaz_bce_loss,\n",
    "                                                           'dice_coef_loss': dice_coef_loss})\n",
    "        \n",
    "    model_checkpoint = ModelCheckpoint(os.path.join(weight_dir, project_name + '.h5'), \n",
    "                                       monitor='val_dice_coef', mode= 'max' , save_best_only=True)\n",
    "\n",
    "    log_dir = 'logs'\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.mkdir(log_dir)\n",
    "    csv_logger = CSVLogger(os.path.join(log_dir,  project_name + '.txt'), separator=',', append=False)\n",
    "\n",
    "    print('-'*30)\n",
    "    print('Fitting model...')\n",
    "    print('-'*30)\n",
    "\n",
    "    steps_per_epoch = np.int(np.ceil((len(train_data_list)*(1+ AUG_RATE) )/batch_size))\n",
    "    validation_steps = np.int(np.ceil(len(val_data_list)/batch_size))\n",
    "\n",
    "    print(\"steps_per_epoch = \", steps_per_epoch)\n",
    "    print(\"validation_steps = \", validation_steps)\n",
    "    \n",
    "    from tensorflow.keras import metrics\n",
    "    \n",
    "    reduce_lr = ReduceLROnPlateau(monitor='val_loss',factor=0.5,patience=5)\n",
    "    ##### set learning rate ################\n",
    "    lr=1e-4\n",
    "    \n",
    "    lr_finder = LRFinder(min_lr=5e-7, \n",
    "                    max_lr=1e-5, \n",
    "                    steps_per_epoch=steps_per_epoch, \n",
    "                    epochs=EPOCHS)\n",
    "    \n",
    "    scheduler = SGDRScheduler(min_lr=5e-7,\n",
    "                        max_lr=1e-5,\n",
    "                        steps_per_epoch=steps_per_epoch,\n",
    "                        lr_decay=0.9,\n",
    "                        cycle_length=5,\n",
    "                        mult_factor=1.5)\n",
    "    \n",
    "    model.compile(optimizer=Adam(lr=lr), \n",
    "                  loss=loss_function, metrics=[metrics.binary_crossentropy, dice_coef])\n",
    "\n",
    "    \n",
    "    #model.fit(imgs_train, imgs_mask_train, batch_size=1, epochs=50, verbose=1, shuffle=True, validation_split=0.10, callbacks=[model_checkpoint, csv_logger])\n",
    "    model.fit(train_dataset, validation_data = valid_dataset, steps_per_epoch = steps_per_epoch,\n",
    "         validation_steps = validation_steps, epochs = EPOCHS, callbacks=[model_checkpoint,reduce_lr,lr_finder,scheduler, csv_logger,PlotLossesKerasTF()],\n",
    "             verbose = 1)\n",
    "\n",
    "    print('-'*30)\n",
    "    print('Training finished')\n",
    "    print('-'*30)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3dDenseUnet\n",
      "------------------------------\n",
      "Creating and compiling model...\n",
      "------------------------------\n",
      "------------------------------\n",
      "Fitting model...\n",
      "------------------------------\n",
      "steps_per_epoch =  54\n",
      "validation_steps =  14\n",
      "Epoch 1/50\n",
      "54/54 [==============================] - 323s 6s/step - loss: 0.8162 - binary_crossentropy: 0.3496 - dice_coef: 0.1842 - val_loss: 0.5002 - val_binary_crossentropy: 0.0467 - val_dice_coef: 0.5050\n",
      "Epoch 2/50\n",
      "54/54 [==============================] - 289s 5s/step - loss: 0.4536 - binary_crossentropy: 0.0452 - dice_coef: 0.5483 - val_loss: 0.4660 - val_binary_crossentropy: 0.0353 - val_dice_coef: 0.5405\n",
      "Epoch 3/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3972 - binary_crossentropy: 0.0342 - dice_coef: 0.6032 - val_loss: 0.3731 - val_binary_crossentropy: 0.0234 - val_dice_coef: 0.6311\n",
      "Epoch 4/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3577 - binary_crossentropy: 0.0311 - dice_coef: 0.6432 - val_loss: 0.3170 - val_binary_crossentropy: 0.0187 - val_dice_coef: 0.6868\n",
      "Epoch 5/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3239 - binary_crossentropy: 0.0268 - dice_coef: 0.6678 - val_loss: 0.3032 - val_binary_crossentropy: 0.0194 - val_dice_coef: 0.7008\n",
      "Epoch 6/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2644 - binary_crossentropy: 0.0219 - dice_coef: 0.7353 - val_loss: 0.2904 - val_binary_crossentropy: 0.0172 - val_dice_coef: 0.7144\n",
      "Epoch 7/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2966 - binary_crossentropy: 0.0251 - dice_coef: 0.7009 - val_loss: 0.3497 - val_binary_crossentropy: 0.0231 - val_dice_coef: 0.6551\n",
      "Epoch 8/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2613 - binary_crossentropy: 0.0215 - dice_coef: 0.7387 - val_loss: 0.2756 - val_binary_crossentropy: 0.0182 - val_dice_coef: 0.7285\n",
      "Epoch 9/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2790 - binary_crossentropy: 0.0220 - dice_coef: 0.7216 - val_loss: 0.2803 - val_binary_crossentropy: 0.0177 - val_dice_coef: 0.7235\n",
      "Epoch 10/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2738 - binary_crossentropy: 0.0223 - dice_coef: 0.7278 - val_loss: 0.3684 - val_binary_crossentropy: 0.0256 - val_dice_coef: 0.6349\n",
      "Epoch 11/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2997 - binary_crossentropy: 0.0216 - dice_coef: 0.7017 - val_loss: 0.2676 - val_binary_crossentropy: 0.0177 - val_dice_coef: 0.7364\n",
      "Epoch 12/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2434 - binary_crossentropy: 0.0209 - dice_coef: 0.7567 - val_loss: 0.2623 - val_binary_crossentropy: 0.0164 - val_dice_coef: 0.7417\n",
      "Epoch 13/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2485 - binary_crossentropy: 0.0214 - dice_coef: 0.7519 - val_loss: 0.2703 - val_binary_crossentropy: 0.0180 - val_dice_coef: 0.7334\n",
      "Epoch 14/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2802 - binary_crossentropy: 0.0220 - dice_coef: 0.7109 - val_loss: 0.2872 - val_binary_crossentropy: 0.0195 - val_dice_coef: 0.7174\n",
      "Epoch 15/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2333 - binary_crossentropy: 0.0192 - dice_coef: 0.7572 - val_loss: 0.2548 - val_binary_crossentropy: 0.0176 - val_dice_coef: 0.7489\n",
      "Epoch 16/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2141 - binary_crossentropy: 0.0180 - dice_coef: 0.7870 - val_loss: 0.2558 - val_binary_crossentropy: 0.0169 - val_dice_coef: 0.7481\n",
      "Epoch 17/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2235 - binary_crossentropy: 0.0177 - dice_coef: 0.7739 - val_loss: 0.2819 - val_binary_crossentropy: 0.0217 - val_dice_coef: 0.7222\n",
      "Epoch 18/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2499 - binary_crossentropy: 0.0211 - dice_coef: 0.7409 - val_loss: 0.2608 - val_binary_crossentropy: 0.0169 - val_dice_coef: 0.7433\n",
      "Epoch 19/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2295 - binary_crossentropy: 0.0179 - dice_coef: 0.7711 - val_loss: 0.2482 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.7553\n",
      "Epoch 20/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2213 - binary_crossentropy: 0.0196 - dice_coef: 0.7764 - val_loss: 0.2453 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7584\n",
      "Epoch 21/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2375 - binary_crossentropy: 0.0178 - dice_coef: 0.7616 - val_loss: 0.2440 - val_binary_crossentropy: 0.0166 - val_dice_coef: 0.7597\n",
      "Epoch 22/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2342 - binary_crossentropy: 0.0183 - dice_coef: 0.7667 - val_loss: 0.2499 - val_binary_crossentropy: 0.0176 - val_dice_coef: 0.7540\n",
      "Epoch 23/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1990 - binary_crossentropy: 0.0176 - dice_coef: 0.8003 - val_loss: 0.2835 - val_binary_crossentropy: 0.0211 - val_dice_coef: 0.7198\n",
      "Epoch 24/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2053 - binary_crossentropy: 0.0177 - dice_coef: 0.7951 - val_loss: 0.2415 - val_binary_crossentropy: 0.0171 - val_dice_coef: 0.7620\n",
      "Epoch 25/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.2169 - binary_crossentropy: 0.0184 - dice_coef: 0.7845 - val_loss: 0.2684 - val_binary_crossentropy: 0.0199 - val_dice_coef: 0.7352\n",
      "Epoch 26/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2065 - binary_crossentropy: 0.0179 - dice_coef: 0.7940 - val_loss: 0.2961 - val_binary_crossentropy: 0.0222 - val_dice_coef: 0.7078\n",
      "Epoch 27/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2138 - binary_crossentropy: 0.0180 - dice_coef: 0.7872 - val_loss: 0.2442 - val_binary_crossentropy: 0.0176 - val_dice_coef: 0.7595\n",
      "Epoch 28/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1932 - binary_crossentropy: 0.0172 - dice_coef: 0.8065 - val_loss: 0.2445 - val_binary_crossentropy: 0.0168 - val_dice_coef: 0.7587\n",
      "Epoch 29/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2077 - binary_crossentropy: 0.0179 - dice_coef: 0.7923 - val_loss: 0.2585 - val_binary_crossentropy: 0.0192 - val_dice_coef: 0.7449\n",
      "Epoch 30/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.2119 - binary_crossentropy: 0.0188 - dice_coef: 0.7890 - val_loss: 0.2453 - val_binary_crossentropy: 0.0188 - val_dice_coef: 0.7583\n",
      "Epoch 31/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1973 - binary_crossentropy: 0.0166 - dice_coef: 0.8032 - val_loss: 0.2537 - val_binary_crossentropy: 0.0184 - val_dice_coef: 0.7497\n",
      "Epoch 32/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1824 - binary_crossentropy: 0.0172 - dice_coef: 0.8177 - val_loss: 0.2679 - val_binary_crossentropy: 0.0205 - val_dice_coef: 0.7360\n",
      "Epoch 33/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1922 - binary_crossentropy: 0.0164 - dice_coef: 0.8086 - val_loss: 0.2644 - val_binary_crossentropy: 0.0194 - val_dice_coef: 0.7390\n",
      "Epoch 34/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.2008 - binary_crossentropy: 0.0151 - dice_coef: 0.8010 - val_loss: 0.2490 - val_binary_crossentropy: 0.0178 - val_dice_coef: 0.7548\n",
      "Epoch 35/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1841 - binary_crossentropy: 0.0151 - dice_coef: 0.8170 - val_loss: 0.2532 - val_binary_crossentropy: 0.0183 - val_dice_coef: 0.7499\n",
      "Epoch 36/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1824 - binary_crossentropy: 0.0157 - dice_coef: 0.8184 - val_loss: 0.2428 - val_binary_crossentropy: 0.0187 - val_dice_coef: 0.7610\n",
      "Epoch 37/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1843 - binary_crossentropy: 0.0150 - dice_coef: 0.8063 - val_loss: 0.2373 - val_binary_crossentropy: 0.0170 - val_dice_coef: 0.7665\n",
      "Epoch 38/50\n",
      "54/54 [==============================] - 283s 5s/step - loss: 0.1643 - binary_crossentropy: 0.0137 - dice_coef: 0.8303 - val_loss: 0.2476 - val_binary_crossentropy: 0.0180 - val_dice_coef: 0.7560\n",
      "Epoch 39/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1660 - binary_crossentropy: 0.0148 - dice_coef: 0.8348 - val_loss: 0.2756 - val_binary_crossentropy: 0.0209 - val_dice_coef: 0.7280\n",
      "Epoch 40/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1605 - binary_crossentropy: 0.0140 - dice_coef: 0.8382 - val_loss: 0.2442 - val_binary_crossentropy: 0.0182 - val_dice_coef: 0.7592\n",
      "Epoch 41/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1872 - binary_crossentropy: 0.0159 - dice_coef: 0.8073 - val_loss: 0.2780 - val_binary_crossentropy: 0.0219 - val_dice_coef: 0.7260\n",
      "Epoch 42/50\n",
      "54/54 [==============================] - 283s 5s/step - loss: 0.1860 - binary_crossentropy: 0.0165 - dice_coef: 0.8146 - val_loss: 0.2997 - val_binary_crossentropy: 0.0238 - val_dice_coef: 0.7036\n",
      "Epoch 43/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1664 - binary_crossentropy: 0.0143 - dice_coef: 0.8328 - val_loss: 0.2610 - val_binary_crossentropy: 0.0196 - val_dice_coef: 0.7427\n",
      "Epoch 44/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1599 - binary_crossentropy: 0.0137 - dice_coef: 0.8401 - val_loss: 0.2435 - val_binary_crossentropy: 0.0191 - val_dice_coef: 0.7603\n",
      "Epoch 45/50\n",
      "54/54 [==============================] - 283s 5s/step - loss: 0.1448 - binary_crossentropy: 0.0129 - dice_coef: 0.8551 - val_loss: 0.2730 - val_binary_crossentropy: 0.0216 - val_dice_coef: 0.7306\n",
      "Epoch 46/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1596 - binary_crossentropy: 0.0129 - dice_coef: 0.8416 - val_loss: 0.2440 - val_binary_crossentropy: 0.0192 - val_dice_coef: 0.7589\n",
      "Epoch 47/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1445 - binary_crossentropy: 0.0126 - dice_coef: 0.8554 - val_loss: 0.2468 - val_binary_crossentropy: 0.0195 - val_dice_coef: 0.7575\n",
      "Epoch 48/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1394 - binary_crossentropy: 0.0121 - dice_coef: 0.8612 - val_loss: 0.2423 - val_binary_crossentropy: 0.0197 - val_dice_coef: 0.7614\n",
      "Epoch 49/50\n",
      "54/54 [==============================] - 283s 5s/step - loss: 0.1514 - binary_crossentropy: 0.0122 - dice_coef: 0.8498 - val_loss: 0.2569 - val_binary_crossentropy: 0.0204 - val_dice_coef: 0.7468\n",
      "Epoch 50/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.1340 - binary_crossentropy: 0.0118 - dice_coef: 0.8658 - val_loss: 0.2574 - val_binary_crossentropy: 0.0195 - val_dice_coef: 0.7466\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "PROJECT_NAME = '3dDenseUnet'\n",
    "train(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3dDenseUnet_att_c1\n",
      "------------------------------\n",
      "Creating and compiling model...\n",
      "------------------------------\n",
      "------------------------------\n",
      "Fitting model...\n",
      "------------------------------\n",
      "steps_per_epoch =  54\n",
      "validation_steps =  14\n",
      "Epoch 1/50\n",
      "54/54 [==============================] - 290s 5s/step - loss: 0.7850 - binary_crossentropy: 0.2819 - dice_coef: 0.2149 - val_loss: 0.4952 - val_binary_crossentropy: 0.0314 - val_dice_coef: 0.5105\n",
      "Epoch 2/50\n",
      "54/54 [==============================] - 289s 5s/step - loss: 0.4357 - binary_crossentropy: 0.0322 - dice_coef: 0.5677 - val_loss: 0.4618 - val_binary_crossentropy: 0.0279 - val_dice_coef: 0.5449\n",
      "Epoch 3/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.3930 - binary_crossentropy: 0.0314 - dice_coef: 0.6052 - val_loss: 0.4339 - val_binary_crossentropy: 0.0299 - val_dice_coef: 0.5725\n",
      "Epoch 4/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.3924 - binary_crossentropy: 0.0304 - dice_coef: 0.6091 - val_loss: 0.4064 - val_binary_crossentropy: 0.0280 - val_dice_coef: 0.5998\n",
      "Epoch 5/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3456 - binary_crossentropy: 0.0290 - dice_coef: 0.6494 - val_loss: 0.3652 - val_binary_crossentropy: 0.0179 - val_dice_coef: 0.6363\n",
      "Epoch 6/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3208 - binary_crossentropy: 0.0205 - dice_coef: 0.6796 - val_loss: 0.3519 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.6510\n",
      "Epoch 7/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.3026 - binary_crossentropy: 0.0201 - dice_coef: 0.6997 - val_loss: 0.3889 - val_binary_crossentropy: 0.0189 - val_dice_coef: 0.6151\n",
      "Epoch 8/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2736 - binary_crossentropy: 0.0188 - dice_coef: 0.7180 - val_loss: 0.2916 - val_binary_crossentropy: 0.0165 - val_dice_coef: 0.7132\n",
      "Epoch 9/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2512 - binary_crossentropy: 0.0188 - dice_coef: 0.7464 - val_loss: 0.3142 - val_binary_crossentropy: 0.0154 - val_dice_coef: 0.6884\n",
      "Epoch 10/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2702 - binary_crossentropy: 0.0183 - dice_coef: 0.7274 - val_loss: 0.2769 - val_binary_crossentropy: 0.0165 - val_dice_coef: 0.7277\n",
      "Epoch 11/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2617 - binary_crossentropy: 0.0174 - dice_coef: 0.7401 - val_loss: 0.2604 - val_binary_crossentropy: 0.0153 - val_dice_coef: 0.7441\n",
      "Epoch 12/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2202 - binary_crossentropy: 0.0161 - dice_coef: 0.7801 - val_loss: 0.2880 - val_binary_crossentropy: 0.0163 - val_dice_coef: 0.7158\n",
      "Epoch 13/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2390 - binary_crossentropy: 0.0162 - dice_coef: 0.7516 - val_loss: 0.2669 - val_binary_crossentropy: 0.0166 - val_dice_coef: 0.7375\n",
      "Epoch 14/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2298 - binary_crossentropy: 0.0164 - dice_coef: 0.7718 - val_loss: 0.2613 - val_binary_crossentropy: 0.0157 - val_dice_coef: 0.7428\n",
      "Epoch 15/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2216 - binary_crossentropy: 0.0156 - dice_coef: 0.7781 - val_loss: 0.2516 - val_binary_crossentropy: 0.0147 - val_dice_coef: 0.7518\n",
      "Epoch 16/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2371 - binary_crossentropy: 0.0167 - dice_coef: 0.7636 - val_loss: 0.2610 - val_binary_crossentropy: 0.0158 - val_dice_coef: 0.7431\n",
      "Epoch 17/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2362 - binary_crossentropy: 0.0172 - dice_coef: 0.7649 - val_loss: 0.2499 - val_binary_crossentropy: 0.0149 - val_dice_coef: 0.7542\n",
      "Epoch 18/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2200 - binary_crossentropy: 0.0152 - dice_coef: 0.7818 - val_loss: 0.2571 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7472\n",
      "Epoch 19/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2154 - binary_crossentropy: 0.0152 - dice_coef: 0.7859 - val_loss: 0.2422 - val_binary_crossentropy: 0.0150 - val_dice_coef: 0.7618\n",
      "Epoch 20/50\n",
      "54/54 [==============================] - 284s 5s/step - loss: 0.2134 - binary_crossentropy: 0.0155 - dice_coef: 0.7787 - val_loss: 0.2457 - val_binary_crossentropy: 0.0150 - val_dice_coef: 0.7583\n",
      "Epoch 21/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2133 - binary_crossentropy: 0.0162 - dice_coef: 0.7879 - val_loss: 0.2413 - val_binary_crossentropy: 0.0151 - val_dice_coef: 0.7629\n",
      "Epoch 22/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2216 - binary_crossentropy: 0.0154 - dice_coef: 0.7791 - val_loss: 0.2542 - val_binary_crossentropy: 0.0182 - val_dice_coef: 0.7484\n",
      "Epoch 23/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2109 - binary_crossentropy: 0.0153 - dice_coef: 0.7863 - val_loss: 0.2524 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.7495\n",
      "Epoch 24/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2080 - binary_crossentropy: 0.0146 - dice_coef: 0.7924 - val_loss: 0.2449 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7582\n",
      "Epoch 25/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1851 - binary_crossentropy: 0.0148 - dice_coef: 0.8154 - val_loss: 0.2498 - val_binary_crossentropy: 0.0169 - val_dice_coef: 0.7535\n",
      "Epoch 26/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1928 - binary_crossentropy: 0.0153 - dice_coef: 0.8082 - val_loss: 0.2544 - val_binary_crossentropy: 0.0164 - val_dice_coef: 0.7481\n",
      "Epoch 27/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1878 - binary_crossentropy: 0.0147 - dice_coef: 0.8117 - val_loss: 0.2491 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.7544\n",
      "Epoch 28/50\n",
      "54/54 [==============================] - 283s 5s/step - loss: 0.1853 - binary_crossentropy: 0.0152 - dice_coef: 0.8047 - val_loss: 0.2420 - val_binary_crossentropy: 0.0172 - val_dice_coef: 0.7614\n",
      "Epoch 29/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1749 - binary_crossentropy: 0.0141 - dice_coef: 0.8256 - val_loss: 0.2453 - val_binary_crossentropy: 0.0183 - val_dice_coef: 0.7581\n",
      "Epoch 30/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1867 - binary_crossentropy: 0.0157 - dice_coef: 0.8132 - val_loss: 0.2914 - val_binary_crossentropy: 0.0225 - val_dice_coef: 0.7120\n",
      "Epoch 31/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.2229 - binary_crossentropy: 0.0180 - dice_coef: 0.7791 - val_loss: 0.2457 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.7579\n",
      "Epoch 32/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1973 - binary_crossentropy: 0.0156 - dice_coef: 0.8027 - val_loss: 0.2347 - val_binary_crossentropy: 0.0161 - val_dice_coef: 0.7681\n",
      "Epoch 33/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1805 - binary_crossentropy: 0.0146 - dice_coef: 0.8175 - val_loss: 0.2537 - val_binary_crossentropy: 0.0185 - val_dice_coef: 0.7498\n",
      "Epoch 34/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1665 - binary_crossentropy: 0.0142 - dice_coef: 0.8341 - val_loss: 0.2502 - val_binary_crossentropy: 0.0188 - val_dice_coef: 0.7535\n",
      "Epoch 35/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1655 - binary_crossentropy: 0.0138 - dice_coef: 0.8292 - val_loss: 0.2575 - val_binary_crossentropy: 0.0191 - val_dice_coef: 0.7465\n",
      "Epoch 36/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1683 - binary_crossentropy: 0.0146 - dice_coef: 0.8327 - val_loss: 0.2494 - val_binary_crossentropy: 0.0188 - val_dice_coef: 0.7532\n",
      "Epoch 37/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1514 - binary_crossentropy: 0.0134 - dice_coef: 0.8487 - val_loss: 0.2319 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7719\n",
      "Epoch 38/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1649 - binary_crossentropy: 0.0135 - dice_coef: 0.8359 - val_loss: 0.2570 - val_binary_crossentropy: 0.0204 - val_dice_coef: 0.7457\n",
      "Epoch 39/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1677 - binary_crossentropy: 0.0151 - dice_coef: 0.8296 - val_loss: 0.2376 - val_binary_crossentropy: 0.0175 - val_dice_coef: 0.7662\n",
      "Epoch 40/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1533 - binary_crossentropy: 0.0131 - dice_coef: 0.8475 - val_loss: 0.2291 - val_binary_crossentropy: 0.0177 - val_dice_coef: 0.7746\n",
      "Epoch 41/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1475 - binary_crossentropy: 0.0136 - dice_coef: 0.8520 - val_loss: 0.2421 - val_binary_crossentropy: 0.0183 - val_dice_coef: 0.7615\n",
      "Epoch 42/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1566 - binary_crossentropy: 0.0139 - dice_coef: 0.8419 - val_loss: 0.2211 - val_binary_crossentropy: 0.0172 - val_dice_coef: 0.7825\n",
      "Epoch 43/50\n",
      "54/54 [==============================] - 291s 5s/step - loss: 0.1544 - binary_crossentropy: 0.0125 - dice_coef: 0.8469 - val_loss: 0.2373 - val_binary_crossentropy: 0.0176 - val_dice_coef: 0.7667\n",
      "Epoch 44/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1443 - binary_crossentropy: 0.0115 - dice_coef: 0.8562 - val_loss: 0.2450 - val_binary_crossentropy: 0.0183 - val_dice_coef: 0.7588\n",
      "Epoch 45/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1434 - binary_crossentropy: 0.0120 - dice_coef: 0.8567 - val_loss: 0.2278 - val_binary_crossentropy: 0.0180 - val_dice_coef: 0.7760\n",
      "Epoch 46/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1302 - binary_crossentropy: 0.0112 - dice_coef: 0.8699 - val_loss: 0.2388 - val_binary_crossentropy: 0.0179 - val_dice_coef: 0.7654\n",
      "Epoch 47/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1169 - binary_crossentropy: 0.0105 - dice_coef: 0.8823 - val_loss: 0.2399 - val_binary_crossentropy: 0.0175 - val_dice_coef: 0.7639\n",
      "Epoch 48/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1301 - binary_crossentropy: 0.0113 - dice_coef: 0.8690 - val_loss: 0.2314 - val_binary_crossentropy: 0.0172 - val_dice_coef: 0.7723\n",
      "Epoch 49/50\n",
      "54/54 [==============================] - 290s 5s/step - loss: 0.1304 - binary_crossentropy: 0.0107 - dice_coef: 0.8705 - val_loss: 0.2361 - val_binary_crossentropy: 0.0177 - val_dice_coef: 0.7677\n",
      "Epoch 50/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1255 - binary_crossentropy: 0.0106 - dice_coef: 0.8750 - val_loss: 0.2502 - val_binary_crossentropy: 0.0203 - val_dice_coef: 0.7531\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='1'\n",
    "PROJECT_NAME = '3dDenseUnet_att_c1'\n",
    "train(True, 1 )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3dDenseUnet_att_c4\n",
      "------------------------------\n",
      "Creating and compiling model...\n",
      "------------------------------\n",
      "------------------------------\n",
      "Fitting model...\n",
      "------------------------------\n",
      "steps_per_epoch =  54\n",
      "validation_steps =  14\n",
      "Epoch 1/50\n",
      "54/54 [==============================] - 292s 5s/step - loss: 0.8801 - binary_crossentropy: 0.4131 - dice_coef: 0.1197 - val_loss: 0.6874 - val_binary_crossentropy: 0.0893 - val_dice_coef: 0.3159\n",
      "Epoch 2/50\n",
      "54/54 [==============================] - 290s 5s/step - loss: 0.4944 - binary_crossentropy: 0.0431 - dice_coef: 0.5013 - val_loss: 0.4698 - val_binary_crossentropy: 0.0294 - val_dice_coef: 0.5373\n",
      "Epoch 3/50\n",
      "54/54 [==============================] - 289s 5s/step - loss: 0.4159 - binary_crossentropy: 0.0313 - dice_coef: 0.5833 - val_loss: 0.4516 - val_binary_crossentropy: 0.0296 - val_dice_coef: 0.5558\n",
      "Epoch 4/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.4085 - binary_crossentropy: 0.0312 - dice_coef: 0.5887 - val_loss: 0.4063 - val_binary_crossentropy: 0.0278 - val_dice_coef: 0.5993\n",
      "Epoch 5/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.3476 - binary_crossentropy: 0.0306 - dice_coef: 0.6443 - val_loss: 0.4450 - val_binary_crossentropy: 0.0329 - val_dice_coef: 0.5604\n",
      "Epoch 6/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3362 - binary_crossentropy: 0.0270 - dice_coef: 0.6611 - val_loss: 0.4125 - val_binary_crossentropy: 0.0260 - val_dice_coef: 0.5921\n",
      "Epoch 7/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.3090 - binary_crossentropy: 0.0226 - dice_coef: 0.6927 - val_loss: 0.3365 - val_binary_crossentropy: 0.0220 - val_dice_coef: 0.6677\n",
      "Epoch 8/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2689 - binary_crossentropy: 0.0214 - dice_coef: 0.7287 - val_loss: 0.3026 - val_binary_crossentropy: 0.0191 - val_dice_coef: 0.7018\n",
      "Epoch 9/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2807 - binary_crossentropy: 0.0211 - dice_coef: 0.7214 - val_loss: 0.3315 - val_binary_crossentropy: 0.0211 - val_dice_coef: 0.6726\n",
      "Epoch 10/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.2792 - binary_crossentropy: 0.0181 - dice_coef: 0.7201 - val_loss: 0.2663 - val_binary_crossentropy: 0.0153 - val_dice_coef: 0.7382\n",
      "Epoch 11/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2506 - binary_crossentropy: 0.0198 - dice_coef: 0.7501 - val_loss: 0.3009 - val_binary_crossentropy: 0.0154 - val_dice_coef: 0.7028\n",
      "Epoch 12/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.2479 - binary_crossentropy: 0.0180 - dice_coef: 0.7519 - val_loss: 0.2619 - val_binary_crossentropy: 0.0157 - val_dice_coef: 0.7424\n",
      "Epoch 13/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2458 - binary_crossentropy: 0.0204 - dice_coef: 0.7521 - val_loss: 0.2662 - val_binary_crossentropy: 0.0159 - val_dice_coef: 0.7383\n",
      "Epoch 14/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2404 - binary_crossentropy: 0.0162 - dice_coef: 0.7600 - val_loss: 0.2794 - val_binary_crossentropy: 0.0179 - val_dice_coef: 0.7249\n",
      "Epoch 15/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.2263 - binary_crossentropy: 0.0161 - dice_coef: 0.7743 - val_loss: 0.2522 - val_binary_crossentropy: 0.0156 - val_dice_coef: 0.7519\n",
      "Epoch 16/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2198 - binary_crossentropy: 0.0165 - dice_coef: 0.7786 - val_loss: 0.2992 - val_binary_crossentropy: 0.0190 - val_dice_coef: 0.7055\n",
      "Epoch 17/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2029 - binary_crossentropy: 0.0155 - dice_coef: 0.7976 - val_loss: 0.2892 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.7143\n",
      "Epoch 18/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2121 - binary_crossentropy: 0.0158 - dice_coef: 0.7782 - val_loss: 0.2647 - val_binary_crossentropy: 0.0161 - val_dice_coef: 0.7395\n",
      "Epoch 19/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2240 - binary_crossentropy: 0.0162 - dice_coef: 0.7776 - val_loss: 0.2580 - val_binary_crossentropy: 0.0166 - val_dice_coef: 0.7461\n",
      "Epoch 20/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.2234 - binary_crossentropy: 0.0171 - dice_coef: 0.7670 - val_loss: 0.2418 - val_binary_crossentropy: 0.0153 - val_dice_coef: 0.7619\n",
      "Epoch 21/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1937 - binary_crossentropy: 0.0158 - dice_coef: 0.7987 - val_loss: 0.2586 - val_binary_crossentropy: 0.0168 - val_dice_coef: 0.7453\n",
      "Epoch 22/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2220 - binary_crossentropy: 0.0184 - dice_coef: 0.7797 - val_loss: 0.2590 - val_binary_crossentropy: 0.0172 - val_dice_coef: 0.7454\n",
      "Epoch 23/50\n",
      "54/54 [==============================] - 289s 5s/step - loss: 0.1995 - binary_crossentropy: 0.0156 - dice_coef: 0.8015 - val_loss: 0.2445 - val_binary_crossentropy: 0.0160 - val_dice_coef: 0.7594\n",
      "Epoch 24/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2033 - binary_crossentropy: 0.0155 - dice_coef: 0.7966 - val_loss: 0.2636 - val_binary_crossentropy: 0.0186 - val_dice_coef: 0.7396\n",
      "Epoch 25/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1882 - binary_crossentropy: 0.0154 - dice_coef: 0.8067 - val_loss: 0.2356 - val_binary_crossentropy: 0.0158 - val_dice_coef: 0.7680\n",
      "Epoch 26/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.2070 - binary_crossentropy: 0.0160 - dice_coef: 0.7942 - val_loss: 0.2649 - val_binary_crossentropy: 0.0182 - val_dice_coef: 0.7397\n",
      "Epoch 27/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.2018 - binary_crossentropy: 0.0163 - dice_coef: 0.7970 - val_loss: 0.2400 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7639\n",
      "Epoch 28/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1989 - binary_crossentropy: 0.0147 - dice_coef: 0.8014 - val_loss: 0.2493 - val_binary_crossentropy: 0.0178 - val_dice_coef: 0.7542\n",
      "Epoch 29/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1764 - binary_crossentropy: 0.0147 - dice_coef: 0.8234 - val_loss: 0.2715 - val_binary_crossentropy: 0.0202 - val_dice_coef: 0.7307\n",
      "Epoch 30/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1904 - binary_crossentropy: 0.0166 - dice_coef: 0.8086 - val_loss: 0.2589 - val_binary_crossentropy: 0.0185 - val_dice_coef: 0.7450\n",
      "Epoch 31/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1760 - binary_crossentropy: 0.0148 - dice_coef: 0.8242 - val_loss: 0.2471 - val_binary_crossentropy: 0.0178 - val_dice_coef: 0.7568\n",
      "Epoch 32/50\n",
      "54/54 [==============================] - 285s 5s/step - loss: 0.1670 - binary_crossentropy: 0.0146 - dice_coef: 0.8328 - val_loss: 0.2373 - val_binary_crossentropy: 0.0174 - val_dice_coef: 0.7664\n",
      "Epoch 33/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1783 - binary_crossentropy: 0.0144 - dice_coef: 0.8223 - val_loss: 0.2402 - val_binary_crossentropy: 0.0171 - val_dice_coef: 0.7633\n",
      "Epoch 34/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1732 - binary_crossentropy: 0.0144 - dice_coef: 0.8186 - val_loss: 0.2426 - val_binary_crossentropy: 0.0185 - val_dice_coef: 0.7610\n",
      "Epoch 35/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1945 - binary_crossentropy: 0.0158 - dice_coef: 0.8021 - val_loss: 0.2458 - val_binary_crossentropy: 0.0177 - val_dice_coef: 0.7583\n",
      "Epoch 36/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1711 - binary_crossentropy: 0.0138 - dice_coef: 0.8187 - val_loss: 0.2420 - val_binary_crossentropy: 0.0180 - val_dice_coef: 0.7617\n",
      "Epoch 37/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1660 - binary_crossentropy: 0.0139 - dice_coef: 0.8321 - val_loss: 0.2524 - val_binary_crossentropy: 0.0187 - val_dice_coef: 0.7510\n",
      "Epoch 38/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1586 - binary_crossentropy: 0.0133 - dice_coef: 0.8413 - val_loss: 0.2439 - val_binary_crossentropy: 0.0176 - val_dice_coef: 0.7599\n",
      "Epoch 39/50\n",
      "54/54 [==============================] - 286s 5s/step - loss: 0.1526 - binary_crossentropy: 0.0125 - dice_coef: 0.8459 - val_loss: 0.2575 - val_binary_crossentropy: 0.0194 - val_dice_coef: 0.7457\n",
      "Epoch 40/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1714 - binary_crossentropy: 0.0131 - dice_coef: 0.8290 - val_loss: 0.2487 - val_binary_crossentropy: 0.0189 - val_dice_coef: 0.7552\n",
      "Epoch 41/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1577 - binary_crossentropy: 0.0136 - dice_coef: 0.8413 - val_loss: 0.2392 - val_binary_crossentropy: 0.0178 - val_dice_coef: 0.7645\n",
      "Epoch 42/50\n",
      "54/54 [==============================] - 290s 5s/step - loss: 0.1436 - binary_crossentropy: 0.0119 - dice_coef: 0.8491 - val_loss: 0.2434 - val_binary_crossentropy: 0.0183 - val_dice_coef: 0.7607\n",
      "Epoch 43/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1577 - binary_crossentropy: 0.0129 - dice_coef: 0.8430 - val_loss: 0.2465 - val_binary_crossentropy: 0.0181 - val_dice_coef: 0.7575\n",
      "Epoch 44/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1384 - binary_crossentropy: 0.0115 - dice_coef: 0.8622 - val_loss: 0.2506 - val_binary_crossentropy: 0.0193 - val_dice_coef: 0.7532\n",
      "Epoch 45/50\n",
      "54/54 [==============================] - 289s 5s/step - loss: 0.1349 - binary_crossentropy: 0.0114 - dice_coef: 0.8652 - val_loss: 0.2770 - val_binary_crossentropy: 0.0212 - val_dice_coef: 0.7269\n",
      "Epoch 46/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1506 - binary_crossentropy: 0.0123 - dice_coef: 0.8505 - val_loss: 0.2441 - val_binary_crossentropy: 0.0180 - val_dice_coef: 0.7594\n",
      "Epoch 47/50\n",
      "54/54 [==============================] - 289s 5s/step - loss: 0.1346 - binary_crossentropy: 0.0115 - dice_coef: 0.8656 - val_loss: 0.2390 - val_binary_crossentropy: 0.0182 - val_dice_coef: 0.7649\n",
      "Epoch 48/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1448 - binary_crossentropy: 0.0116 - dice_coef: 0.8560 - val_loss: 0.2363 - val_binary_crossentropy: 0.0182 - val_dice_coef: 0.7672\n",
      "Epoch 49/50\n",
      "54/54 [==============================] - 288s 5s/step - loss: 0.1283 - binary_crossentropy: 0.0107 - dice_coef: 0.8721 - val_loss: 0.2522 - val_binary_crossentropy: 0.0195 - val_dice_coef: 0.7509\n",
      "Epoch 50/50\n",
      "54/54 [==============================] - 287s 5s/step - loss: 0.1127 - binary_crossentropy: 0.0103 - dice_coef: 0.8874 - val_loss: 0.2514 - val_binary_crossentropy: 0.0196 - val_dice_coef: 0.7524\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "PROJECT_NAME = '3dDenseUnet_att_c4'\n",
    "train(True, 4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3dDenseUnet_att_hyper_c1\n",
      "------------------------------\n",
      "Creating and compiling model...\n",
      "------------------------------\n",
      "------------------------------\n",
      "Fitting model...\n",
      "------------------------------\n",
      "steps_per_epoch =  54\n",
      "validation_steps =  14\n",
      "Epoch 1/50\n",
      "54/54 [==============================] - 281s 5s/step - loss: 0.7869 - binary_crossentropy: 0.1692 - dice_coef: 0.2250 - val_loss: 0.5204 - val_binary_crossentropy: 0.0218 - val_dice_coef: 0.4893\n",
      "Epoch 2/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.4578 - binary_crossentropy: 0.0275 - dice_coef: 0.5483 - val_loss: 0.5274 - val_binary_crossentropy: 0.0278 - val_dice_coef: 0.4866\n",
      "Epoch 3/50\n",
      "54/54 [==============================] - 280s 5s/step - loss: 0.3954 - binary_crossentropy: 0.0227 - dice_coef: 0.6019 - val_loss: 0.4431 - val_binary_crossentropy: 0.0242 - val_dice_coef: 0.5658\n",
      "Epoch 4/50\n",
      "54/54 [==============================] - 280s 5s/step - loss: 0.3448 - binary_crossentropy: 0.0206 - dice_coef: 0.6590 - val_loss: 0.4207 - val_binary_crossentropy: 0.0201 - val_dice_coef: 0.5907\n",
      "Epoch 5/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.3341 - binary_crossentropy: 0.0207 - dice_coef: 0.6714 - val_loss: 0.3812 - val_binary_crossentropy: 0.0180 - val_dice_coef: 0.6287\n",
      "Epoch 6/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.3107 - binary_crossentropy: 0.0174 - dice_coef: 0.6856 - val_loss: 0.3181 - val_binary_crossentropy: 0.0131 - val_dice_coef: 0.6894\n",
      "Epoch 7/50\n",
      "54/54 [==============================] - 280s 5s/step - loss: 0.2954 - binary_crossentropy: 0.0176 - dice_coef: 0.7084 - val_loss: 0.2895 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7169\n",
      "Epoch 8/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2757 - binary_crossentropy: 0.0157 - dice_coef: 0.7293 - val_loss: 0.2981 - val_binary_crossentropy: 0.0162 - val_dice_coef: 0.7084\n",
      "Epoch 9/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2697 - binary_crossentropy: 0.0174 - dice_coef: 0.7247 - val_loss: 0.3069 - val_binary_crossentropy: 0.0178 - val_dice_coef: 0.6998\n",
      "Epoch 10/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.2763 - binary_crossentropy: 0.0161 - dice_coef: 0.7256 - val_loss: 0.2790 - val_binary_crossentropy: 0.0156 - val_dice_coef: 0.7275\n",
      "Epoch 11/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.2576 - binary_crossentropy: 0.0161 - dice_coef: 0.7474 - val_loss: 0.2632 - val_binary_crossentropy: 0.0147 - val_dice_coef: 0.7431\n",
      "Epoch 12/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2832 - binary_crossentropy: 0.0159 - dice_coef: 0.7183 - val_loss: 0.2829 - val_binary_crossentropy: 0.0165 - val_dice_coef: 0.7239\n",
      "Epoch 13/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2568 - binary_crossentropy: 0.0155 - dice_coef: 0.7413 - val_loss: 0.3079 - val_binary_crossentropy: 0.0134 - val_dice_coef: 0.6989\n",
      "Epoch 14/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2582 - binary_crossentropy: 0.0148 - dice_coef: 0.7463 - val_loss: 0.2687 - val_binary_crossentropy: 0.0147 - val_dice_coef: 0.7380\n",
      "Epoch 15/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.2372 - binary_crossentropy: 0.0151 - dice_coef: 0.7631 - val_loss: 0.2659 - val_binary_crossentropy: 0.0142 - val_dice_coef: 0.7407\n",
      "Epoch 16/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2285 - binary_crossentropy: 0.0139 - dice_coef: 0.7741 - val_loss: 0.2702 - val_binary_crossentropy: 0.0144 - val_dice_coef: 0.7368\n",
      "Epoch 17/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2069 - binary_crossentropy: 0.0130 - dice_coef: 0.7961 - val_loss: 0.2789 - val_binary_crossentropy: 0.0168 - val_dice_coef: 0.7272\n",
      "Epoch 18/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.2139 - binary_crossentropy: 0.0137 - dice_coef: 0.7909 - val_loss: 0.2541 - val_binary_crossentropy: 0.0145 - val_dice_coef: 0.7522\n",
      "Epoch 19/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2374 - binary_crossentropy: 0.0142 - dice_coef: 0.7663 - val_loss: 0.2677 - val_binary_crossentropy: 0.0128 - val_dice_coef: 0.7390\n",
      "Epoch 20/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.2250 - binary_crossentropy: 0.0134 - dice_coef: 0.7788 - val_loss: 0.2633 - val_binary_crossentropy: 0.0127 - val_dice_coef: 0.7436\n",
      "Epoch 21/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.2139 - binary_crossentropy: 0.0129 - dice_coef: 0.7905 - val_loss: 0.2747 - val_binary_crossentropy: 0.0127 - val_dice_coef: 0.7321\n",
      "Epoch 22/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2096 - binary_crossentropy: 0.0133 - dice_coef: 0.7925 - val_loss: 0.2672 - val_binary_crossentropy: 0.0158 - val_dice_coef: 0.7387\n",
      "Epoch 23/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.2043 - binary_crossentropy: 0.0127 - dice_coef: 0.7995 - val_loss: 0.2493 - val_binary_crossentropy: 0.0140 - val_dice_coef: 0.7575\n",
      "Epoch 24/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1968 - binary_crossentropy: 0.0130 - dice_coef: 0.8059 - val_loss: 0.2612 - val_binary_crossentropy: 0.0135 - val_dice_coef: 0.7459\n",
      "Epoch 25/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1838 - binary_crossentropy: 0.0121 - dice_coef: 0.8190 - val_loss: 0.2575 - val_binary_crossentropy: 0.0141 - val_dice_coef: 0.7494\n",
      "Epoch 26/50\n",
      "54/54 [==============================] - 279s 5s/step - loss: 0.2163 - binary_crossentropy: 0.0139 - dice_coef: 0.7811 - val_loss: 0.3142 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.6954\n",
      "Epoch 27/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.3211 - binary_crossentropy: 0.0235 - dice_coef: 0.6758 - val_loss: 0.2670 - val_binary_crossentropy: 0.0126 - val_dice_coef: 0.7401\n",
      "Epoch 28/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.2630 - binary_crossentropy: 0.0162 - dice_coef: 0.7389 - val_loss: 0.2521 - val_binary_crossentropy: 0.0143 - val_dice_coef: 0.7546\n",
      "Epoch 29/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.2213 - binary_crossentropy: 0.0135 - dice_coef: 0.7828 - val_loss: 0.2602 - val_binary_crossentropy: 0.0160 - val_dice_coef: 0.7463\n",
      "Epoch 30/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.2190 - binary_crossentropy: 0.0133 - dice_coef: 0.7807 - val_loss: 0.2513 - val_binary_crossentropy: 0.0138 - val_dice_coef: 0.7553\n",
      "Epoch 31/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.2179 - binary_crossentropy: 0.0144 - dice_coef: 0.7797 - val_loss: 0.2581 - val_binary_crossentropy: 0.0141 - val_dice_coef: 0.7486\n",
      "Epoch 32/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.2297 - binary_crossentropy: 0.0138 - dice_coef: 0.7637 - val_loss: 0.2661 - val_binary_crossentropy: 0.0136 - val_dice_coef: 0.7411\n",
      "Epoch 33/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.2102 - binary_crossentropy: 0.0128 - dice_coef: 0.7936 - val_loss: 0.2525 - val_binary_crossentropy: 0.0155 - val_dice_coef: 0.7542\n",
      "Epoch 34/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.2178 - binary_crossentropy: 0.0132 - dice_coef: 0.7815 - val_loss: 0.2519 - val_binary_crossentropy: 0.0151 - val_dice_coef: 0.7553\n",
      "Epoch 35/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.2098 - binary_crossentropy: 0.0132 - dice_coef: 0.7944 - val_loss: 0.2505 - val_binary_crossentropy: 0.0144 - val_dice_coef: 0.7561\n",
      "Epoch 36/50\n",
      "54/54 [==============================] - 275s 5s/step - loss: 0.1971 - binary_crossentropy: 0.0121 - dice_coef: 0.8064 - val_loss: 0.2641 - val_binary_crossentropy: 0.0137 - val_dice_coef: 0.7437\n",
      "Epoch 37/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.1832 - binary_crossentropy: 0.0119 - dice_coef: 0.8207 - val_loss: 0.2580 - val_binary_crossentropy: 0.0161 - val_dice_coef: 0.7491\n",
      "Epoch 38/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1844 - binary_crossentropy: 0.0117 - dice_coef: 0.8186 - val_loss: 0.2583 - val_binary_crossentropy: 0.0161 - val_dice_coef: 0.7484\n",
      "Epoch 39/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.1786 - binary_crossentropy: 0.0121 - dice_coef: 0.8141 - val_loss: 0.2645 - val_binary_crossentropy: 0.0162 - val_dice_coef: 0.7430\n",
      "Epoch 40/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1944 - binary_crossentropy: 0.0118 - dice_coef: 0.8088 - val_loss: 0.2581 - val_binary_crossentropy: 0.0169 - val_dice_coef: 0.7487\n",
      "Epoch 41/50\n",
      "54/54 [==============================] - 278s 5s/step - loss: 0.1785 - binary_crossentropy: 0.0117 - dice_coef: 0.8253 - val_loss: 0.2475 - val_binary_crossentropy: 0.0150 - val_dice_coef: 0.7599\n",
      "Epoch 42/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.1674 - binary_crossentropy: 0.0111 - dice_coef: 0.8249 - val_loss: 0.2524 - val_binary_crossentropy: 0.0145 - val_dice_coef: 0.7542\n",
      "Epoch 43/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1662 - binary_crossentropy: 0.0110 - dice_coef: 0.8371 - val_loss: 0.2821 - val_binary_crossentropy: 0.0188 - val_dice_coef: 0.7245\n",
      "Epoch 44/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.1737 - binary_crossentropy: 0.0113 - dice_coef: 0.8300 - val_loss: 0.2541 - val_binary_crossentropy: 0.0167 - val_dice_coef: 0.7526\n",
      "Epoch 45/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1689 - binary_crossentropy: 0.0112 - dice_coef: 0.8346 - val_loss: 0.2623 - val_binary_crossentropy: 0.0178 - val_dice_coef: 0.7447\n",
      "Epoch 46/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1721 - binary_crossentropy: 0.0110 - dice_coef: 0.8279 - val_loss: 0.2559 - val_binary_crossentropy: 0.0166 - val_dice_coef: 0.7511\n",
      "Epoch 47/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1724 - binary_crossentropy: 0.0112 - dice_coef: 0.8304 - val_loss: 0.2634 - val_binary_crossentropy: 0.0173 - val_dice_coef: 0.7434\n",
      "Epoch 48/50\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.1677 - binary_crossentropy: 0.0106 - dice_coef: 0.8357 - val_loss: 0.2529 - val_binary_crossentropy: 0.0165 - val_dice_coef: 0.7539\n",
      "Epoch 49/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1448 - binary_crossentropy: 0.0098 - dice_coef: 0.8579 - val_loss: 0.2542 - val_binary_crossentropy: 0.0163 - val_dice_coef: 0.7533\n",
      "Epoch 50/50\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1387 - binary_crossentropy: 0.0097 - dice_coef: 0.8641 - val_loss: 0.2459 - val_binary_crossentropy: 0.0154 - val_dice_coef: 0.7613\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='1'\n",
    "PROJECT_NAME = '3dDenseUnet_att_hyper_c1'\n",
    "train(att_input= True, inter_channel = 1, hyper= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_crossentropy\n",
      "\ttraining         \t (min:    0.011, max:    0.351, cur:    0.011)\n",
      "\tvalidation       \t (min:    0.015, max:    0.057, cur:    0.020)\n",
      "dice_coef\n",
      "\ttraining         \t (min:    0.170, max:    0.873, cur:    0.873)\n",
      "\tvalidation       \t (min:    0.448, max:    0.763, cur:    0.745)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.127, max:    0.830, cur:    0.127)\n",
      "\tvalidation       \t (min:    0.241, max:    0.557, cur:    0.259)\n",
      "54/54 [==============================] - 277s 5s/step - loss: 0.1275 - binary_crossentropy: 0.0113 - dice_coef: 0.8728 - val_loss: 0.2585 - val_binary_crossentropy: 0.0202 - val_dice_coef: 0.7450\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='1'\n",
    "PROJECT_NAME = '3dDenseUnet_att_hyper_c1_dropout'\n",
    "train(att_input= True, inter_channel = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_crossentropy\n",
      "\ttraining         \t (min:    0.011, max:    0.351, cur:    0.013)\n",
      "\tvalidation       \t (min:    0.015, max:    0.057, cur:    0.020)\n",
      "dice_coef\n",
      "\ttraining         \t (min:    0.170, max:    0.873, cur:    0.855)\n",
      "\tvalidation       \t (min:    0.448, max:    0.767, cur:    0.757)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.127, max:    0.830, cur:    0.145)\n",
      "\tvalidation       \t (min:    0.236, max:    0.557, cur:    0.246)\n",
      "54/54 [==============================] - 276s 5s/step - loss: 0.1447 - binary_crossentropy: 0.0132 - dice_coef: 0.8548 - val_loss: 0.2464 - val_binary_crossentropy: 0.0199 - val_dice_coef: 0.7573\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "PROJECT_NAME = '3dDenseUnet_dropout'\n",
    "train(att_input= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_crossentropy\n",
      "\ttraining         \t (min:    0.017, max:    0.701, cur:    0.018)\n",
      "\tvalidation       \t (min:    0.014, max:    0.620, cur:    0.017)\n",
      "dice_coef\n",
      "\ttraining         \t (min:    0.017, max:    0.770, cur:    0.765)\n",
      "\tvalidation       \t (min:    0.000, max:    0.750, cur:    0.736)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.230, max:    0.983, cur:    0.236)\n",
      "\tvalidation       \t (min:    0.253, max:    1.000, cur:    0.266)\n",
      "67/67 [==============================] - 348s 5s/step - loss: 0.2362 - binary_crossentropy: 0.0179 - dice_coef: 0.7645 - val_loss: 0.2661 - val_binary_crossentropy: 0.0166 - val_dice_coef: 0.7362\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "PROJECT_NAME = '3dDenseUnet_dropout_augmentation'\n",
    "train(att_input= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_crossentropy\n",
      "\ttraining         \t (min:    0.016, max:    0.727, cur:    0.016)\n",
      "\tvalidation       \t (min:    0.014, max:    0.635, cur:    0.018)\n",
      "dice_coef\n",
      "\ttraining         \t (min:    0.017, max:    0.825, cur:    0.810)\n",
      "\tvalidation       \t (min:    0.000, max:    0.756, cur:    0.752)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.174, max:    0.983, cur:    0.190)\n",
      "\tvalidation       \t (min:    0.247, max:    1.000, cur:    0.252)\n",
      "67/67 [==============================] - 343s 5s/step - loss: 0.1904 - binary_crossentropy: 0.0158 - dice_coef: 0.8101 - val_loss: 0.2515 - val_binary_crossentropy: 0.0181 - val_dice_coef: 0.7516\n",
      "------------------------------\n",
      "Training finished\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 100\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "PROJECT_NAME = '3dDenseUnet_dropout_augmentation'\n",
    "train(att_input= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_crossentropy\n",
      "\ttraining         \t (min:    0.012, max:    0.644, cur:    0.018)\n",
      "\tvalidation       \t (min:    0.014, max:    0.644, cur:    0.017)\n",
      "dice_coef\n",
      "\ttraining         \t (min:    0.000, max:    0.873, cur:    0.786)\n",
      "\tvalidation       \t (min:    0.000, max:    0.772, cur:    0.755)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.127, max:    1.000, cur:    0.215)\n",
      "\tvalidation       \t (min:    0.232, max:    1.000, cur:    0.248)\n",
      "lr\n",
      "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n",
      "67/67 [==============================] - 364s 5s/step - loss: 0.2148 - binary_crossentropy: 0.0177 - dice_coef: 0.7858 - val_loss: 0.2484 - val_binary_crossentropy: 0.0170 - val_dice_coef: 0.7551 - lr: 1.0000e-04\n",
      "Epoch 14/100\n",
      "40/67 [================>.............] - ETA: 2:01 - loss: 0.2244 - binary_crossentropy: 0.0178 - dice_coef: 0.7756"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-1c971831b754>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CUDA_VISIBLE_DEVICES\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'1'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mPROJECT_NAME\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'3dDenseUnet_dropout_elastic_augmentation.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matt_input\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minter_channel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdice_coef_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPROJECT_NAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-33-5b9c33c04d6e>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(att_input, inter_channel, loss_function, retrain, L2)\u001b[0m\n\u001b[1;32m     94\u001b[0m     model.fit(train_dataset, validation_data = valid_dataset, steps_per_epoch = steps_per_epoch,\n\u001b[1;32m     95\u001b[0m          \u001b[0mvalidation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreduce_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcsv_logger\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPlotLossesKerasTF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m              verbose = 1)\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "EPOCHS = 100\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='1'\n",
    "PROJECT_NAME = '3dDenseUnet_dropout_elastic_augmentation.h5'\n",
    "train(att_input= False, inter_channel = 1, loss_function= dice_coef_loss, retrain = PROJECT_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_crossentropy\n",
      "\ttraining         \t (min:    0.010, max:    0.708, cur:    0.056)\n",
      "\tvalidation       \t (min:    0.011, max:    0.709, cur:    0.018)\n",
      "dice_coef\n",
      "\ttraining         \t (min:    0.000, max:    0.873, cur:    0.679)\n",
      "\tvalidation       \t (min:    0.000, max:    0.772, cur:    0.762)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.127, max:    1.320, cur:    0.323)\n",
      "\tvalidation       \t (min:    0.232, max:    1.032, cur:    0.241)\n",
      "lr\n",
      "\tlr               \t (min:    0.000, max:    0.001, cur:    0.000)\n",
      "70/70 [==============================] - 336s 5s/step - loss: 0.3235 - binary_crossentropy: 0.0561 - dice_coef: 0.6788 - val_loss: 0.2414 - val_binary_crossentropy: 0.0177 - val_dice_coef: 0.7622 - lr: 6.7178e-06\n",
      "Epoch 3/50\n",
      "60/70 [========================>.....] - ETA: 40s - loss: 0.2277 - binary_crossentropy: 0.0357 - dice_coef: 0.7743"
     ]
    }
   ],
   "source": [
    "\n",
    "EPOCHS = 50\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='1'\n",
    "PROJECT_NAME = '3dDenseUnet_dropout_elas_aug_window'\n",
    "train(att_input= False, loss_function= dice_coef_loss,retrain = PROJECT_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}