{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport sys\nimport random\n\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nfrom skimage.io import imshow\n\nimport tensorflow as tf\nfrom tensorflow import keras","execution_count":1,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train_data_path = \"../input/stage1_train/\"\nimage_size = 256\n\ntrain_ids = next(os.walk(train_data_path))[1]\n\nx_train = np.zeros((len(train_ids),image_size, image_size, 3), dtype = np.uint8)\ny_train = np.zeros((len(train_ids),image_size, image_size, 1))\n\nfor i, id_ in enumerate(train_ids):\n    image_path = train_data_path+\"{}/images/{}\".format(id_,id_)\n    img = cv2.imread(image_path+\".png\", 1)\n    img = cv2.resize(img, (image_size, image_size))\n    x_train[i] = img\n    \n    height, width, _ = img.shape\n    label = np.zeros((height, width, 1))\n    mask_path = train_data_path+\"{}/masks/\".format(id_)\n    for mask_file in next(os.walk(mask_path))[2]:\n        mask = cv2.imread(mask_path+mask_file, 0)\n        mask = cv2.resize(mask, (image_size, image_size))\n        mask = np.expand_dims(mask, axis=-1)\n        label = np.maximum(label, mask)\n    y_train[i] = label","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data_path = \"../input/stage1_test/\"\n\ntest_ids=next(os.walk(test_data_path))[1]\nx_test = np.zeros((len(test_ids), image_size, image_size, 3), dtype=np.uint8)\ntest_sizes = []\nfor i, id_ in enumerate(test_ids):\n    test_image_path = test_data_path+\"{}/images/{}\".format(id_,id_)\n    test_image = cv2.imread(test_image_path+\".png\", 1)\n    test_sizes.append([test_image.shape[0], test_image.shape[1]])\n    test_image = cv2.resize(test_image, (image_size, image_size))\n    x_test[i]=test_image","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"r = random.randint(0, len(x_train)-1)\n\nfig = plt.figure()\nfig.subplots_adjust(hspace=0.4, wspace=0.4)\nax = fig.add_subplot(1, 2, 1)\nax.imshow(x_train[r])\nax = fig.add_subplot(1, 2, 2)\nax.imshow(np.reshape(y_train[r]*255, (image_size, image_size)), cmap=\"gray\")","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f186808d978>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\n\ntrain_gen = ImageDataGenerator(rescale = 1./255,\n                              rotation_range=30,\n                              height_shift_range=0.3,\n                              zoom_range=0.3,\n                              horizontal_flip=True,\n                              width_shift_range=0.3,\n                              fill_mode='reflect')\n\nval_gen = ImageDataGenerator(rescale=1./255)","execution_count":5,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nxt, xv, yt, yv = train_test_split(x_train, y_train, test_size=0.2)","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = yv[22][:,:,0]\nimshow(y)\nplt.show()","execution_count":7,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/skimage/io/_plugins/matplotlib_plugin.py:78: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n  warn(\"Float image out of standard range; displaying \"\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAUgAAAEYCAYAAAA+mm/EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAF9hJREFUeJzt3X+oXOWdx/H3x9S1+AtN04YkhtWWuBCX7dUNNqAUi9tGw0IUFokLmm1dImwEBRca7R8VJOAuVVmhlb2iGBdbK6gYSrZpGiwi1B9R0pgfVW81YmJMNlVUVjY19373j/OMnt47586ZuTNnzrn384LDnHnmzJwnTvzkec7znGcUEZiZ2VQnDbsCZmZ15YA0MyvggDQzK+CANDMr4IA0MyvggDQzKzCwgJR0haTXJI1J2jio85iZDYoGMQ9S0jzgdeDbwEHgJeDaiNjX95OZmQ3IoFqQFwNjEfFmRPwJeAxYM6BzmZkNxBcG9LlLgHdyzw8C38gfIGk9sB5gHvP+9lTOHFBVzOrn/L/5pKvjX9996oBqMjPT/Tle3n38WER8ufV81bdOiz++P176s1/efXxbRFwxsxrOzKACsqOIGAVGAc7U/PiGLh9WVcwqt23brq6OX7V4ZEA16c22d1v1/1LhMfMWjb2df37s/XFe2HZO6XOcvOgPC3qqXB8Nqot9CFiae35OKjOb8z4Pl2bqvf7BeEyU3jqRtFTSM5L2Sdor6eZUfoekQ5J2pW117j23pYHj1ySt6nSOQbUgXwKWSTqPLBjXAv84oHOZWUVmEu4BTNDXQeETwK0R8YqkM4CXJW1Pr90bET/KHyxpOVkWXQAsBn4t6fyIKOz3DyQgI+KEpJuAbcA84KGI2DuIc5lZc0zQuWVYVkQcBg6n/Y8l7Scb/yiyBngsIo4Db0kaIxtQ/m3RGwY2DzIitkbE+RHxtYjYNKjzmFkzBMF4lN+ABZJ25rb1RZ8t6VzgQuCFVHSTpN2SHpJ0diprN3g8XaAOb5DGzOaeLrvYxyJiRaeDJJ0OPAHcEhEfSbofuJOsV38ncDfwvR6q64A0s2oEMN7fa5BIOpksHB+NiCcBIuJI7vUHgF+kp10PHvtebLMKNX0Ee6YmiNJbJ5IEPAjsj4h7cuWLcoddDexJ+1uAtZJOSQPIy4AXpzuHW5BmNVe3OZC9CmhdW+yXS4DrgFcltf7luR24VtJIOuUB4EaAiNgr6XFgH9kI+IbpRrDBAWlWqVWLRxrbiuxHvfs3hg0R8RygNi9tneY9m4DSg8YOSLMamy2tR0ij2H2+BjlovgZpVrGyoVe3cJxxfQLGu9jqwAFpNgSdwqZu4dgP2Z005bc6cECaDUlRCM7GcMyI8S62OvA1SLMhmr1hOFUAEzXpOpflgDSz0sqMwn8e+mNTXqtLy7AsB6SZdWVyq7cVmJ1aw9mdNA5IM5tDurlMMBEOSDOzKdyCNDMrEIjxhk2ccUCaWWXcxTYza8NdbDOzQmI83MU2M5sigE+ZN+xqdMUBaWaViHAL0sys0ISvQZqZTZUN0rgFaWbWhrvYZmZtZetBOiDNzNoa90RxM7OpfKuhmdk0JnwN0sxsKo9im5kVCORrkGZmRTyKbWbWRgSeB2lm1p58q6GZWTuBW5BmZoU8im1m1kYg/+SCmVkRtyDNzNoIfCeNmVkB+Ue7zMzamXMtSEkHgI+BceBERKyQNB/4OXAucAC4JiI+mFk1zTLb3t0FwKrFI0OuifViLrYgvxURx3LPNwI7IuIuSRvT8+/34Tw2h7WCcfJzB2VzRKhxLchB1HYNsDntbwauGsA5bA6ZHI5lX7P6GY+TSm91MNNaBPArSS9LWp/KFkbE4bT/HrCw3RslrZe0U9LOTzk+w2rYXOaQbIZAfBrzSm91MNMu9qURcUjSV4Dtkn6ffzEiQlK0e2NEjAKjAGdqfttjzMqG37Z3d7m7XXPZIE2zrkHOqAUZEYfS41HgKeBi4IikRQDp8ehMK2lWhluS9TfOSaW3TiQtlfSMpH2S9kq6OZXPl7Rd0hvp8exULkn3SRqTtFvSRZ3O0XNASjpN0hmtfeA7wB5gC7AuHbYOeLrXc5jZ7NG61bDsVsIJ4NaIWA6sBDZIWs7nA8XLgB3pOcCVwLK0rQfu73SCmXSxFwJPSWp9zk8j4peSXgIel3QD8DZwzQzOYWazSD8XzE1jHYfT/seS9gNLyAaKL0uHbQZ+QzaTZg3wSEQE8LyksyQtyo2ZTNFzQEbEm8DX25T/Ebi81881s9kpWzC3q2uQCyTtzD0fTWMXU0g6F7gQeIHigeIlwDu5tx1MZf0PSDOzbnU5SHMsIlZ0OkjS6cATwC0R8VHq1QLTDxSXUY/JRmYFPDI9e2TXIE8qvZUh6WSycHw0Ip5MxUUDxYeApbm3n5PKCjkgrfYckrPHeFqwoszWibKm4oPA/oi4J/dS0UDxFuD6NJq9EvhwuuuP4C62zSIO0nobwDzIS4DrgFclteZ43Q7cRfuB4q3AamAM+AT4bqcTOCCtEVYtHvE8x8br773YEfEcFDY1pwwUp9HrDd2cwwFpjdFqIbYLSrcem8G/amg2YA7DZuphms/QOSDNrDJNW+6s0QHprpZZc/hXDStSZn1AB6VZ/TTtGmSz2rtd8IinWb20pvn0cbGKgZu1AQkOSbO66fedNINWj1p0odvQc0ia1UQXrce6tCAbeQ3SzJon8DXIWnIr0qwe3II0M2tjzv0mTZO4FWk2fG5Bmpm14YniZmZFAk7UZPpOWc2qLb5DxqypmjhR3C1IM6tMXYKvLAekmVXC1yDNzKYRDQvIxl2DBF+HNGuqCVR6q4NGBiR0H5IOVbPhimjeIE1jAxLKh57D0aweIlR6q4NGByR0Dj+Ho1ldeDWfociHYP6WQodj//jnLawf6tIyLGtWBGSe/6ftv6L72Le9u8v/va20Ji5WMesC0vrLi3xY30Q2UNMkDkgzq0xdpu+U1fhBGhsutzCtrMCj2DYHOSStnOaNYjsgbVoehLF+iii/1YED0vrCrUgro2ldbA/SmFklspZhPYKvLAekmVWmLtcWy3IX2zrydUjrl6Zdg3QL0swq07QudscWpKSHJB2VtCdXNl/SdklvpMezU7kk3SdpTNJuSRcNsvJWHbcibaaC8gM0dQnSMl3sh4ErJpVtBHZExDJgR3oOcCWwLG3rgfv7U02rg+lC0gFqZUQXWx10DMiIeBZ4f1LxGmBz2t8MXJUrfyQyzwNnSVrUr8ra8LULQoejlRJzZ5rPwog4nPbfAxam/SXAO7njDqayw0wiaT1ZK5MvcmqP1bBhcCBar2KiHsFX1owHaSIiJHXdIo6IUWAU4EzNr0uL2swGqC6j02X1Os3nSKvrnB6PpvJDwNLcceekMjOb4/q9WEXBAPIdkg5J2pW21bnXbksDyK9JWlWmzr0G5BZgXdpfBzydK78+jWavBD7MdcXNbC4LIFR+6+xhpg4gA9wbESNp2wogaTmwFrggvecnkuZ1OkGZaT4/A34L/JWkg5JuAO4Cvi3pDeDv0nOArcCbwBjwAPAvnT7fzOaOfk4ULxhALrIGeCwijkfEW2QZdXGnN3W8BhkR1xa8dHmbYwPY0OkzzWyO6u4a5AJJO3PPR9PYRSc3Sboe2AncGhEfkA0WP587pjWAPC3fSWNmFel6+s6xiFjR5UnuB+4ki+I7gbuB73X5GZ/xvdhmVp0BzxSPiCMRMR4RE2SX+Vrd6J4GkB2QZlaNCiaKT7ox5WqgNcK9BVgr6RRJ55Hd7fdip89zF9vMqtPHeZBpAPkysmuVB4EfApdJGklnOgDcCBAReyU9DuwDTgAbImK80zkckGZWof7dSVMwgPzgNMdvAjZ1cw4HpJlVp2F30jggzaw6DkgzszZad9I0iAPSzCrTtMUqHJBmVh0HpJlZAXexzcza637l2OFyQJpZNer0YzMlOSDNrCKl13msDQekmVXHLUgzswIOSDOzAg5IM7M2fCeNmVkxT/MxMyvigDQza88tSDOzIr4GWQ/b3t3VtnzV4pGKa2JmgO+kqYOiYMy/7pA0GxIH5HB0CsbJxzokzarXtGuQs+JnX7sJx5m8x8xmaMC/i91vjQ/ImQSdQ9KsYg0LyEZ3sR1wZs2haF4Xu9EBaWYN07BpPo3vYs+UW6FmFXIX28ysPXexzcyKOCDNzNrwIE11+nHt0JPFzSrWsIBs7CDNTMPN4Wg2BA0bpGlsQELvIedwNBuO1lzIMlsdNLaL3TI57Dp1vR2OZlZW4wNyMgegWY3VpGVYVqMDcnJr0eFoVmM16jqX1fEapKSHJB2VtCdXdoekQ5J2pW117rXbJI1Jek3SqkFV3HfAmDXQLBykeRi4ok35vRExkratAJKWA2uBC9J7fiJpXr8q2+JwNGuo2RaQEfEs8H7Jz1sDPBYRxyPiLWAMuHgG9SvN3WuzehPNG8WeyTSfmyTtTl3ws1PZEuCd3DEHU9kUktZL2ilp56ccn0E1HI5mjTHbWpAF7ge+BowAh4G7u/2AiBiNiBURseJkTumxGg5Hs8YI0ET5rQ56GsWOiCOtfUkPAL9ITw8BS3OHnpPK+sqhaNZQfWwZSnoI+HvgaET8dSqbD/wcOBc4AFwTER9IEvAfwGrgE+CfIuKVTufoqQUpaVHu6dVAa4R7C7BW0imSzgOWAS/2cg4zm336fA3yYaYOIG8EdkTEMmBHeg5wJVkeLQPWk/WCO+rYgpT0M+AyYIGkg8APgcskjZD9e3AAuBEgIvZKehzYB5wANkTEeJmKmNkc0McWZEQ8K+ncScVryPIKYDPwG+D7qfyRiAjgeUlnSVoUEYenO0fHgIyIa9sUPzjN8ZuATZ0+18zmmO4HXxZI2pl7PhoRox3eszAXeu8BC9N+0QDyzALSzKxfupy+cywiVvR6rogIaWYThhq9mo+ZNczgp/kcaY2RpMejqbynAWQHpJlVpoKJ4luAdWl/HfB0rvx6ZVYCH3a6/gjuYptZlfo7zafdAPJdwOOSbgDeBq5Jh28lm+IzRjbN57tlzuGANLNq9PkOmYIBZIDL2xwbwIZuz+GANLNKKG1N4oA0s+rU5B7rshyQZlaZuqzSU5YD0syq44A0MyvggDQza6NGC+GW5YA0s+o4IM3M2nML0sysiAPSzKw9tyDNzNqp0Y9xleWANLPqOCDNzKZq/S52kzggzaw6DkgzszYCNNGshHRAmlll3MU2MyvigDQza88tSDOzIg5IM7M2vJqPmdk0HJBmZlN5oriZ2XSiWQnpgDSzyrgFaWbWjlfzMTMrpolh16A7Dkgzq45bkGZm7fkapJlZO4FHsc3MirgFaWZWxAFpZjaV76QxMysS4WuQZmZFmtaCPKnTAZKWSnpG0j5JeyXdnMrnS9ou6Y30eHYql6T7JI1J2i3pokH/IcysIaKLrQY6BiRwArg1IpYDK4ENkpYDG4EdEbEM2JGeA1wJLEvbeuD+vtfazBpJUX6rg44BGRGHI+KVtP8xsB9YAqwBNqfDNgNXpf01wCOReR44S9KivtfczJolgIkov9VAmRbkZySdC1wIvAAsjIjD6aX3gIVpfwnwTu5tB1OZmc11Detilx6kkXQ68ARwS0R8JOmz1yIipO4axZLWk3XB+SKndvNWM2uoWfm72JJOJgvHRyPiyVR8RNKiiDicutBHU/khYGnu7eeksj8TEaPAKMCZmt+s/2pm1pN+X1uUdAD4GBgHTkTECknzgZ8D5wIHgGsi4oNePr/MKLaAB4H9EXFP7qUtwLq0vw54Old+fRrNXgl8mOuKm9lc1U33ursg/VZEjETEivS8aAC5a2VakJcA1wGvStqVym4H7gIel3QD8DZwTXptK7AaGAM+Ab7ba+XMbPbI7qSppLO4Brgs7W8GfgN8v5cP6hiQEfEc2Z+tncvbHB/Ahl4qY2azXP8XzA3gV2kM5D/TpbuiAeSu+U4aM6tMly3IBZJ25p6PpgDMuzQiDkn6CrBd0u/zL/YygJzngDSzanR/bfFY7rpi+4+MOJQej0p6CriY4gHkrnU1D9LMrHfx+YIVZbYOJJ0m6YzWPvAdYA/FA8hdcwvSzCrT52k+C4Gn0pzsLwA/jYhfSnqJ9gPIXXNAmll1+jiKHRFvAl9vU/5H2gwg98IBaWbVCP/sq5lZMS+Ya2ZWoFn56IA0s+pUdCdN3zggzaw6Dkiri23v7ppStmrxyBBqYkZaMHfYleiOA3KWaheO+XIHpVVNROO62L6TZhYqCsdujzHruz7eSVMFB+Qs003wOSStcg5IaxKHpFWmdQ2y7FYDvgZpZpVp2jVIB6SZVadhAekutrmbbRXp73JnVXBAzjKevmO1FTQuIN3FNrPKaLwewVeWW5CzkFuRVlsNa0E6IGcph6TVTgATUX6rAXexzWFqFalPy7AstyBnMQef1Y672FYnnULSIWqValhAuos9BzgErRZa1yAbxAFpZhUJiJrcZF2SA9LMqlOTrnNZDkgzq4a72GZm03AL0sysgAPSzKyd+kzfKcsBaWbVCGDCo9hmZu25BWlmVsABaWbWTn1W6SnLAWlz0uSfmfDtmBUIiIbdSePFKmzOafcbPP5dnoo0bD1IB6TNKdMFoUOyAg1bzadjQEpaKukZSfsk7ZV0cyq/Q9IhSbvStjr3ntskjUl6TdKqQf4BzPrJITlAEdk0n7JbDZS5BnkCuDUiXpF0BvCypO3ptXsj4kf5gyUtB9YCFwCLgV9LOj8ixvtZcbNuOfxqoCYtw7I6BmREHAYOp/2PJe0HlkzzljXAYxFxHHhL0hhwMfDbPtTXzBosatIyLKura5CSzgUuBF5IRTdJ2i3pIUlnp7IlwDu5tx1k+kA1szmhi+uPNWlplg5ISacDTwC3RMRHwP3A14ARshbm3d2cWNJ6STsl7fyU49281axr7l7XQADj4+W3Gig1D1LSyWTh+GhEPAkQEUdyrz8A/CI9PQQszb39nFT2ZyJiFBgFOFPz6/HPhZkNTABRk+k7ZZUZxRbwILA/Iu7JlS/KHXY1sCftbwHWSjpF0nnAMuDF/lXZzBop0k8ulN1KkHRFmi0zJmljv6tcpgV5CXAd8KqkVj/lduBaSSNk/zAcAG4EiIi9kh4H9pGNgG/wCLYN26rFI+5m10A/W5CS5gE/Br5NNtbxkqQtEbGvX+coM4r9HKA2L22d5j2bgE0zqJeZzUb9vdXwYmAsIt4EkPQY2SyavgWkogajRZL+B/hf4Niw6zIDC3D9h8n1H6529f/LiPhy64mkX6bjyvoi8H+556Np7KL1ef8AXBER/5yeXwd8IyJu6rbyRWqxWEVEfFnSzohYMey69Mr1Hy7Xf7jK1D8irqiqPv3ie7HNrKlKzZiZCQekmTXVS8AySedJ+guyW5y39PMEtehiJ6OdD6k113+4XP/hqrz+EXFC0k3ANmAe8FBE7O3nOWoxSGNmVkfuYpuZFXBAmpkVGHpADvpWoUGQdEDSq2mh4J2pbL6k7ZLeSI9nd/qcqqTVlo5K2pMra1tfZe5L38duSRcNr+af1bVd/RuzYPM0i0434juY04tmR8TQNrILq38Avgr8BfA7YPkw61Sy3geABZPK/h3YmPY3Av827Hrm6vZN4CJgT6f6AquB/ya7e2ol8EJN638H8K9tjl2e/h6dApyX/n7NG3L9FwEXpf0zgNdTPRvxHUxT/8Z8B71uw25BfnarUET8CWjdKtREa4DNaX8zcNUQ6/JnIuJZ4P1JxUX1XQM8EpnngbMmLUxSuYL6F/lsweaIeAtoLdg8NBFxOCJeSfsfA61FpxvxHUxT/yK1+w56NeyAbOriugH8StLLktansoWRrb4O8B6wcDhVK62ovk36Thq3YPOkRacb9x3MtUWzhx2QTXVpRFwEXAlskPTN/IuR9TMaM3+qafVNZrRg8zC0WXT6M034Dvq9aHYTDDsgB36r0CBExKH0eBR4iqz7cKTVDUqPR4dXw1KK6tuI7yQijkTEeGS/RP8An3fhaln/dotO06DvoGjR7CZ9B70YdkAO/FahfpN0mrJfd0TSacB3yBYL3gKsS4etA54eTg1LK6rvFuD6NJK6Evgw1w2sDTVowWap/aLTNOQ7KKp/k76Dng17lIhsxO51spGuHwy7PiXq+1WyEbrfAXtbdQa+BOwA3gB+Dcwfdl1zdf4ZWRfoU7LrQTcU1Zds5PTH6ft4FVhR0/r/V6rfbrL/IRfljv9Bqv9rwJU1qP+lZN3n3cCutK1uyncwTf0b8x30uvlWQzOzAsPuYpuZ1ZYD0sysgAPSzKyAA9LMrIAD0sysgAPSzKyAA9LMrMD/A7cSNq1XTjSFAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(xt.shape)\nprint(len(yt))\nprint(len(xv))\nprint(yv.shape)","execution_count":8,"outputs":[{"output_type":"stream","text":"(536, 256, 256, 3)\n536\n134\n(134, 256, 256, 1)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"seed=42\ndef combineGenerator(gen1, gen2):\n    while True:\n        yield(gen1.next(), gen2.next())\n\ntrain_generator_image = train_gen.flow(xt,batch_size=16, seed=seed, shuffle=True)\ntrain_generator_mask = train_gen.flow(yt, batch_size=16, seed=seed, shuffle=True)\ntrain_generator = combineGenerator(train_generator_image, train_generator_mask)\nvalidation_generator_image = val_gen.flow(xv, batch_size=16, seed=seed, shuffle=True)\nvalidation_generator_mask = val_gen.flow(yv, batch_size=16, seed=seed, shuffle=True)\nvalidation_generator = combineGenerator(validation_generator_image, validation_generator_mask)","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"a, b = next(validation_generator)\nprint(a.shape, b.shape)","execution_count":10,"outputs":[{"output_type":"stream","text":"(16, 256, 256, 3) (16, 256, 256, 1)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"img1, mask1 = next(train_generator)\n\nfig=plt.figure(figsize=(12,12))\ncolumns = 2\nrows = 4\ncounter = 0\nc = 0\nfor i in range(1, columns*rows +1):\n    #img = np.random.randint(10, size=(h,w))\n    fig.add_subplot(rows, columns, i)\n    if counter%2 == 0:\n        plt.imshow(img1[c])\n    else:\n        plt.imshow(mask1[c][:,:,0])\n        c = c + 1\n    counter = counter + 1\n    \nplt.show()","execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x864 with 8 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def bn_act(x, act=True):\n    x = keras.layers.BatchNormalization()(x)\n    if act==True:\n        x = keras.layers.Activation('relu')(x)\n    return x\n\ndef conv_block(x, filters, kernel_size=(3,3), padding='same', strides=1):\n    conv = bn_act(x)\n    conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(conv)\n    return conv\n\ndef stem(x, filters, kernel_size=(3,3), padding='same', strides=1):\n    conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)\n    conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n    \n    shortcut = keras.layers.Conv2D(filters, kernel_size=(1,1), padding=padding, strides=strides)(x)\n    shortcut = bn_act(shortcut, act=False)\n    \n    output = keras.layers.Add()([conv, shortcut])\n    return output\n\ndef residual_block(x, filters, kernel_size=(3,3), padding='same', strides=1):\n    conv = conv_block(x, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n    conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=1)\n    \n    shortcut = keras.layers.Conv2D(filters, kernel_size=(1,1), padding=padding, strides=strides)(x)\n    shortcut = bn_act(shortcut, act=False)\n    \n    output = keras.layers.Add()([shortcut, conv])\n    return output\n\ndef up_sampling_concat_block(x, x_skip):\n    up_sample = keras.layers.UpSampling2D((2,2))(x)\n    concat = keras.layers.Concatenate()([up_sample, x_skip])\n    return concat\n    ","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def ResUnet():\n    f = [16,32,64,128,256]\n    img_=256\n    inputs = keras.layers.Input((img_, img_,3))\n    \n    #encoding\n    e0 = inputs\n    e1 = stem(e0, f[0])\n    e2 = residual_block(e1, f[1], strides=2)\n    e3 = residual_block(e2, f[2], strides=2)\n    e4 = residual_block(e3, f[3], strides=2)\n    e5 = residual_block(e4, f[4], strides=2)\n    \n    #bridge\n    b0 = conv_block(e5, f[4], strides=1)\n    b1 = conv_block(b0, f[4], strides=1)\n    \n    #decoder\n    u0 = up_sampling_concat_block(b1, e4)\n    d0 = residual_block(u0, f[4])\n    \n    u1 = up_sampling_concat_block(d0, e3)\n    d1 = residual_block(u1, f[3])\n    \n    u2 = up_sampling_concat_block(d1, e2)\n    d2 = residual_block(u2, f[2])\n    \n    u3 = up_sampling_concat_block(d2, e1)\n    d3 = residual_block(u3, f[1])\n    \n    output = keras.layers.Conv2D(1, (1,1), padding='same', activation='sigmoid')(d3)\n    model = keras.models.Model(inputs, output)\n    return model","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"smooth = 1.\n\ndef dice_coef(y_true, y_pred):\n    y_true_f = tf.layers.flatten(y_true)\n    y_pred_f = tf.layers.flatten(y_pred)\n    intersection = tf.reduce_sum(y_true_f * y_pred_f)\n    return (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n\n\ndef dice_coef_loss(y_true, y_pred):\n    return 1.0 - dice_coef(y_true, y_pred)","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.python.keras.optimizers import adam\n\nmodel = ResUnet()\nmodel.compile(optimizer=adam(lr=0.0005), loss=dice_coef_loss, metrics=[dice_coef])\nmodel.summary()","execution_count":62,"outputs":[{"output_type":"stream","text":"__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_6 (InputLayer)            (None, 256, 256, 3)  0                                            \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D)             (None, 256, 256, 16) 448         input_6[0][0]                    \n__________________________________________________________________________________________________\nbatch_normalization_v1_140 (Bat (None, 256, 256, 16) 64          conv2d_150[0][0]                 \n__________________________________________________________________________________________________\nactivation_95 (Activation)      (None, 256, 256, 16) 0           batch_normalization_v1_140[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D)             (None, 256, 256, 16) 64          input_6[0][0]                    \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D)             (None, 256, 256, 16) 2320        activation_95[0][0]              \n__________________________________________________________________________________________________\nbatch_normalization_v1_141 (Bat (None, 256, 256, 16) 64          conv2d_152[0][0]                 \n__________________________________________________________________________________________________\nadd_45 (Add)                    (None, 256, 256, 16) 0           conv2d_151[0][0]                 \n                                                                 batch_normalization_v1_141[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_142 (Bat (None, 256, 256, 16) 64          add_45[0][0]                     \n__________________________________________________________________________________________________\nactivation_96 (Activation)      (None, 256, 256, 16) 0           batch_normalization_v1_142[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D)             (None, 128, 128, 32) 4640        activation_96[0][0]              \n__________________________________________________________________________________________________\nbatch_normalization_v1_143 (Bat (None, 128, 128, 32) 128         conv2d_153[0][0]                 \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D)             (None, 128, 128, 32) 544         add_45[0][0]                     \n__________________________________________________________________________________________________\nactivation_97 (Activation)      (None, 128, 128, 32) 0           batch_normalization_v1_143[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_144 (Bat (None, 128, 128, 32) 128         conv2d_155[0][0]                 \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D)             (None, 128, 128, 32) 9248        activation_97[0][0]              \n__________________________________________________________________________________________________\nadd_46 (Add)                    (None, 128, 128, 32) 0           batch_normalization_v1_144[0][0] \n                                                                 conv2d_154[0][0]                 \n__________________________________________________________________________________________________\nbatch_normalization_v1_145 (Bat (None, 128, 128, 32) 128         add_46[0][0]                     \n__________________________________________________________________________________________________\nactivation_98 (Activation)      (None, 128, 128, 32) 0           batch_normalization_v1_145[0][0] \n__________________________________________________________________________________________________\nconv2d_156 (Conv2D)             (None, 64, 64, 64)   18496       activation_98[0][0]              \n__________________________________________________________________________________________________\nbatch_normalization_v1_146 (Bat (None, 64, 64, 64)   256         conv2d_156[0][0]                 \n__________________________________________________________________________________________________\nconv2d_158 (Conv2D)             (None, 64, 64, 64)   2112        add_46[0][0]                     \n__________________________________________________________________________________________________\nactivation_99 (Activation)      (None, 64, 64, 64)   0           batch_normalization_v1_146[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_147 (Bat (None, 64, 64, 64)   256         conv2d_158[0][0]                 \n__________________________________________________________________________________________________\nconv2d_157 (Conv2D)             (None, 64, 64, 64)   36928       activation_99[0][0]              \n__________________________________________________________________________________________________\nadd_47 (Add)                    (None, 64, 64, 64)   0           batch_normalization_v1_147[0][0] \n                                                                 conv2d_157[0][0]                 \n__________________________________________________________________________________________________\nbatch_normalization_v1_148 (Bat (None, 64, 64, 64)   256         add_47[0][0]                     \n__________________________________________________________________________________________________\nactivation_100 (Activation)     (None, 64, 64, 64)   0           batch_normalization_v1_148[0][0] \n__________________________________________________________________________________________________\nconv2d_159 (Conv2D)             (None, 32, 32, 128)  73856       activation_100[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_149 (Bat (None, 32, 32, 128)  512         conv2d_159[0][0]                 \n__________________________________________________________________________________________________\nconv2d_161 (Conv2D)             (None, 32, 32, 128)  8320        add_47[0][0]                     \n__________________________________________________________________________________________________\nactivation_101 (Activation)     (None, 32, 32, 128)  0           batch_normalization_v1_149[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_150 (Bat (None, 32, 32, 128)  512         conv2d_161[0][0]                 \n__________________________________________________________________________________________________\nconv2d_160 (Conv2D)             (None, 32, 32, 128)  147584      activation_101[0][0]             \n__________________________________________________________________________________________________\nadd_48 (Add)                    (None, 32, 32, 128)  0           batch_normalization_v1_150[0][0] \n                                                                 conv2d_160[0][0]                 \n__________________________________________________________________________________________________\nbatch_normalization_v1_151 (Bat (None, 32, 32, 128)  512         add_48[0][0]                     \n__________________________________________________________________________________________________\nactivation_102 (Activation)     (None, 32, 32, 128)  0           batch_normalization_v1_151[0][0] \n__________________________________________________________________________________________________\nconv2d_162 (Conv2D)             (None, 16, 16, 256)  295168      activation_102[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_152 (Bat (None, 16, 16, 256)  1024        conv2d_162[0][0]                 \n__________________________________________________________________________________________________\nconv2d_164 (Conv2D)             (None, 16, 16, 256)  33024       add_48[0][0]                     \n__________________________________________________________________________________________________\nactivation_103 (Activation)     (None, 16, 16, 256)  0           batch_normalization_v1_152[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_153 (Bat (None, 16, 16, 256)  1024        conv2d_164[0][0]                 \n__________________________________________________________________________________________________\nconv2d_163 (Conv2D)             (None, 16, 16, 256)  590080      activation_103[0][0]             \n__________________________________________________________________________________________________\nadd_49 (Add)                    (None, 16, 16, 256)  0           batch_normalization_v1_153[0][0] \n                                                                 conv2d_163[0][0]                 \n__________________________________________________________________________________________________\nbatch_normalization_v1_154 (Bat (None, 16, 16, 256)  1024        add_49[0][0]                     \n__________________________________________________________________________________________________\nactivation_104 (Activation)     (None, 16, 16, 256)  0           batch_normalization_v1_154[0][0] \n__________________________________________________________________________________________________\nconv2d_165 (Conv2D)             (None, 16, 16, 256)  590080      activation_104[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_155 (Bat (None, 16, 16, 256)  1024        conv2d_165[0][0]                 \n__________________________________________________________________________________________________\nactivation_105 (Activation)     (None, 16, 16, 256)  0           batch_normalization_v1_155[0][0] \n__________________________________________________________________________________________________\nconv2d_166 (Conv2D)             (None, 16, 16, 256)  590080      activation_105[0][0]             \n__________________________________________________________________________________________________\nup_sampling2d_20 (UpSampling2D) (None, 32, 32, 256)  0           conv2d_166[0][0]                 \n__________________________________________________________________________________________________\nconcatenate_20 (Concatenate)    (None, 32, 32, 384)  0           up_sampling2d_20[0][0]           \n                                                                 add_48[0][0]                     \n__________________________________________________________________________________________________\nbatch_normalization_v1_156 (Bat (None, 32, 32, 384)  1536        concatenate_20[0][0]             \n__________________________________________________________________________________________________\nactivation_106 (Activation)     (None, 32, 32, 384)  0           batch_normalization_v1_156[0][0] \n__________________________________________________________________________________________________\nconv2d_167 (Conv2D)             (None, 32, 32, 256)  884992      activation_106[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_157 (Bat (None, 32, 32, 256)  1024        conv2d_167[0][0]                 \n__________________________________________________________________________________________________\nconv2d_169 (Conv2D)             (None, 32, 32, 256)  98560       concatenate_20[0][0]             \n__________________________________________________________________________________________________\nactivation_107 (Activation)     (None, 32, 32, 256)  0           batch_normalization_v1_157[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_158 (Bat (None, 32, 32, 256)  1024        conv2d_169[0][0]                 \n__________________________________________________________________________________________________\nconv2d_168 (Conv2D)             (None, 32, 32, 256)  590080      activation_107[0][0]             \n__________________________________________________________________________________________________\nadd_50 (Add)                    (None, 32, 32, 256)  0           batch_normalization_v1_158[0][0] \n                                                                 conv2d_168[0][0]                 \n__________________________________________________________________________________________________\nup_sampling2d_21 (UpSampling2D) (None, 64, 64, 256)  0           add_50[0][0]                     \n__________________________________________________________________________________________________\nconcatenate_21 (Concatenate)    (None, 64, 64, 320)  0           up_sampling2d_21[0][0]           \n                                                                 add_47[0][0]                     \n__________________________________________________________________________________________________\nbatch_normalization_v1_159 (Bat (None, 64, 64, 320)  1280        concatenate_21[0][0]             \n__________________________________________________________________________________________________\nactivation_108 (Activation)     (None, 64, 64, 320)  0           batch_normalization_v1_159[0][0] \n__________________________________________________________________________________________________\nconv2d_170 (Conv2D)             (None, 64, 64, 128)  368768      activation_108[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_160 (Bat (None, 64, 64, 128)  512         conv2d_170[0][0]                 \n__________________________________________________________________________________________________\nconv2d_172 (Conv2D)             (None, 64, 64, 128)  41088       concatenate_21[0][0]             \n__________________________________________________________________________________________________\nactivation_109 (Activation)     (None, 64, 64, 128)  0           batch_normalization_v1_160[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_161 (Bat (None, 64, 64, 128)  512         conv2d_172[0][0]                 \n__________________________________________________________________________________________________\nconv2d_171 (Conv2D)             (None, 64, 64, 128)  147584      activation_109[0][0]             \n__________________________________________________________________________________________________\nadd_51 (Add)                    (None, 64, 64, 128)  0           batch_normalization_v1_161[0][0] \n                                                                 conv2d_171[0][0]                 \n__________________________________________________________________________________________________\nup_sampling2d_22 (UpSampling2D) (None, 128, 128, 128 0           add_51[0][0]                     \n__________________________________________________________________________________________________\nconcatenate_22 (Concatenate)    (None, 128, 128, 160 0           up_sampling2d_22[0][0]           \n                                                                 add_46[0][0]                     \n__________________________________________________________________________________________________\nbatch_normalization_v1_162 (Bat (None, 128, 128, 160 640         concatenate_22[0][0]             \n__________________________________________________________________________________________________\nactivation_110 (Activation)     (None, 128, 128, 160 0           batch_normalization_v1_162[0][0] \n__________________________________________________________________________________________________\nconv2d_173 (Conv2D)             (None, 128, 128, 64) 92224       activation_110[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_163 (Bat (None, 128, 128, 64) 256         conv2d_173[0][0]                 \n__________________________________________________________________________________________________\nconv2d_175 (Conv2D)             (None, 128, 128, 64) 10304       concatenate_22[0][0]             \n__________________________________________________________________________________________________\nactivation_111 (Activation)     (None, 128, 128, 64) 0           batch_normalization_v1_163[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_164 (Bat (None, 128, 128, 64) 256         conv2d_175[0][0]                 \n__________________________________________________________________________________________________\nconv2d_174 (Conv2D)             (None, 128, 128, 64) 36928       activation_111[0][0]             \n__________________________________________________________________________________________________\nadd_52 (Add)                    (None, 128, 128, 64) 0           batch_normalization_v1_164[0][0] \n                                                                 conv2d_174[0][0]                 \n__________________________________________________________________________________________________\nup_sampling2d_23 (UpSampling2D) (None, 256, 256, 64) 0           add_52[0][0]                     \n__________________________________________________________________________________________________\nconcatenate_23 (Concatenate)    (None, 256, 256, 80) 0           up_sampling2d_23[0][0]           \n                                                                 add_45[0][0]                     \n__________________________________________________________________________________________________\nbatch_normalization_v1_165 (Bat (None, 256, 256, 80) 320         concatenate_23[0][0]             \n__________________________________________________________________________________________________\nactivation_112 (Activation)     (None, 256, 256, 80) 0           batch_normalization_v1_165[0][0] \n__________________________________________________________________________________________________\nconv2d_176 (Conv2D)             (None, 256, 256, 32) 23072       activation_112[0][0]             \n__________________________________________________________________________________________________\nbatch_normalization_v1_166 (Bat (None, 256, 256, 32) 128         conv2d_176[0][0]                 \n__________________________________________________________________________________________________\nconv2d_178 (Conv2D)             (None, 256, 256, 32) 2592        concatenate_23[0][0]             \n__________________________________________________________________________________________________\nactivation_113 (Activation)     (None, 256, 256, 32) 0           batch_normalization_v1_166[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_v1_167 (Bat (None, 256, 256, 32) 128         conv2d_178[0][0]                 \n__________________________________________________________________________________________________\nconv2d_177 (Conv2D)             (None, 256, 256, 32) 9248        activation_113[0][0]             \n__________________________________________________________________________________________________\nadd_53 (Add)                    (None, 256, 256, 32) 0           batch_normalization_v1_167[0][0] \n                                                                 conv2d_177[0][0]                 \n__________________________________________________________________________________________________\nconv2d_179 (Conv2D)             (None, 256, 256, 1)  33          add_53[0][0]                     \n==================================================================================================\nTotal params: 4,723,057\nTrainable params: 4,715,761\nNon-trainable params: 7,296\n__________________________________________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.callbacks import  ModelCheckpoint\n\ncallbacks=[ModelCheckpoint(monitor='val_loss',\n                             filepath='dsb.h5',\n                             save_best_only=True)]\ntrain_steps = len(xt)//16\nval_steps = len(xv)//16\nhistory = model.fit_generator(train_generator, steps_per_epoch = train_steps, epochs = 20,\n                              validation_data = validation_generator, validation_steps = val_steps, callbacks=callbacks)","execution_count":63,"outputs":[{"output_type":"stream","text":"Epoch 1/30\n8/8 [==============================] - 3s 331ms/step - loss: 0.7925 - dice_coef: 0.2075\n33/33 [==============================] - 86s 3s/step - loss: 0.3183 - dice_coef: 0.6817 - val_loss: 0.7925 - val_dice_coef: 0.2075\nEpoch 2/30\n8/8 [==============================] - 1s 81ms/step - loss: 0.7876 - dice_coef: 0.2124\n33/33 [==============================] - 9s 286ms/step - loss: 0.2621 - dice_coef: 0.7376 - val_loss: 0.7876 - val_dice_coef: 0.2124\nEpoch 3/30\n8/8 [==============================] - 1s 75ms/step - loss: 0.9643 - dice_coef: 0.0357\n33/33 [==============================] - 11s 328ms/step - loss: 0.2113 - dice_coef: 0.7887 - val_loss: 0.9643 - val_dice_coef: 0.0357\nEpoch 4/30\n8/8 [==============================] - 1s 75ms/step - loss: 0.7853 - dice_coef: 0.2147\n33/33 [==============================] - 11s 345ms/step - loss: 0.2036 - dice_coef: 0.7965 - val_loss: 0.7853 - val_dice_coef: 0.2147\nEpoch 5/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.9947 - dice_coef: 0.0053\n33/33 [==============================] - 11s 328ms/step - loss: 0.1726 - dice_coef: 0.8274 - val_loss: 0.9947 - val_dice_coef: 0.0053\nEpoch 6/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.9677 - dice_coef: 0.0323\n33/33 [==============================] - 11s 331ms/step - loss: 0.1764 - dice_coef: 0.8237 - val_loss: 0.9677 - val_dice_coef: 0.0323\nEpoch 7/30\n8/8 [==============================] - 1s 78ms/step - loss: 0.9911 - dice_coef: 0.0089\n33/33 [==============================] - 11s 331ms/step - loss: 0.1652 - dice_coef: 0.8348 - val_loss: 0.9911 - val_dice_coef: 0.0089\nEpoch 8/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.7438 - dice_coef: 0.2562\n33/33 [==============================] - 12s 349ms/step - loss: 0.1611 - dice_coef: 0.8390 - val_loss: 0.7438 - val_dice_coef: 0.2562\nEpoch 9/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.9974 - dice_coef: 0.0026\n33/33 [==============================] - 11s 331ms/step - loss: 0.1652 - dice_coef: 0.8348 - val_loss: 0.9974 - val_dice_coef: 0.0026\nEpoch 10/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.6089 - dice_coef: 0.3911\n33/33 [==============================] - 12s 351ms/step - loss: 0.1607 - dice_coef: 0.8393 - val_loss: 0.6089 - val_dice_coef: 0.3911\nEpoch 11/30\n8/8 [==============================] - 1s 81ms/step - loss: 0.6202 - dice_coef: 0.3798\n33/33 [==============================] - 11s 329ms/step - loss: 0.1515 - dice_coef: 0.8485 - val_loss: 0.6202 - val_dice_coef: 0.3798\nEpoch 12/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.4190 - dice_coef: 0.5810\n33/33 [==============================] - 11s 344ms/step - loss: 0.1536 - dice_coef: 0.8464 - val_loss: 0.4190 - val_dice_coef: 0.5810\nEpoch 13/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.3923 - dice_coef: 0.6077\n33/33 [==============================] - 11s 341ms/step - loss: 0.1517 - dice_coef: 0.8483 - val_loss: 0.3923 - val_dice_coef: 0.6077\nEpoch 14/30\n8/8 [==============================] - 1s 73ms/step - loss: 0.3034 - dice_coef: 0.6966\n33/33 [==============================] - 11s 337ms/step - loss: 0.1545 - dice_coef: 0.8455 - val_loss: 0.3034 - val_dice_coef: 0.6966\nEpoch 15/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.5189 - dice_coef: 0.4811\n33/33 [==============================] - 11s 327ms/step - loss: 0.1505 - dice_coef: 0.8495 - val_loss: 0.5189 - val_dice_coef: 0.4811\nEpoch 16/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.2685 - dice_coef: 0.7315\n33/33 [==============================] - 11s 346ms/step - loss: 0.1438 - dice_coef: 0.8562 - val_loss: 0.2685 - val_dice_coef: 0.7315\nEpoch 17/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.1981 - dice_coef: 0.8019\n33/33 [==============================] - 11s 341ms/step - loss: 0.1458 - dice_coef: 0.8541 - val_loss: 0.1981 - val_dice_coef: 0.8019\nEpoch 18/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.8568 - dice_coef: 0.1432\n33/33 [==============================] - 11s 325ms/step - loss: 0.1544 - dice_coef: 0.8457 - val_loss: 0.8568 - val_dice_coef: 0.1432\nEpoch 19/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.1177 - dice_coef: 0.8823\n33/33 [==============================] - 11s 343ms/step - loss: 0.1497 - dice_coef: 0.8503 - val_loss: 0.1177 - val_dice_coef: 0.8823\nEpoch 20/30\n8/8 [==============================] - 1s 82ms/step - loss: 0.0958 - dice_coef: 0.9042\n33/33 [==============================] - 11s 344ms/step - loss: 0.1516 - dice_coef: 0.8484 - val_loss: 0.0958 - val_dice_coef: 0.9042\nEpoch 21/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.0989 - dice_coef: 0.9011\n33/33 [==============================] - 11s 327ms/step - loss: 0.1511 - dice_coef: 0.8489 - val_loss: 0.0989 - val_dice_coef: 0.9011\nEpoch 22/30\n8/8 [==============================] - 1s 78ms/step - loss: 0.1013 - dice_coef: 0.8987\n33/33 [==============================] - 11s 330ms/step - loss: 0.1477 - dice_coef: 0.8523 - val_loss: 0.1013 - val_dice_coef: 0.8987\nEpoch 23/30\n8/8 [==============================] - 1s 74ms/step - loss: 0.0874 - dice_coef: 0.9126\n33/33 [==============================] - 12s 350ms/step - loss: 0.1441 - dice_coef: 0.8559 - val_loss: 0.0874 - val_dice_coef: 0.9126\nEpoch 24/30\n8/8 [==============================] - 1s 73ms/step - loss: 0.6319 - dice_coef: 0.3681\n33/33 [==============================] - 11s 327ms/step - loss: 0.8092 - dice_coef: 0.1911 - val_loss: 0.6319 - val_dice_coef: 0.3681\nEpoch 25/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.7892 - dice_coef: 0.2108\n33/33 [==============================] - 11s 332ms/step - loss: 0.3742 - dice_coef: 0.6256 - val_loss: 0.7892 - val_dice_coef: 0.2108\nEpoch 26/30\n8/8 [==============================] - 1s 77ms/step - loss: 0.7127 - dice_coef: 0.2873\n33/33 [==============================] - 11s 331ms/step - loss: 0.1877 - dice_coef: 0.8122 - val_loss: 0.7127 - val_dice_coef: 0.2873\nEpoch 27/30\n8/8 [==============================] - 1s 75ms/step - loss: 0.1038 - dice_coef: 0.8962\n33/33 [==============================] - 11s 329ms/step - loss: 0.1573 - dice_coef: 0.8428 - val_loss: 0.1038 - val_dice_coef: 0.8962\nEpoch 28/30\n8/8 [==============================] - 1s 76ms/step - loss: 0.1021 - dice_coef: 0.8979\n33/33 [==============================] - 11s 328ms/step - loss: 0.1473 - dice_coef: 0.8527 - val_loss: 0.1021 - val_dice_coef: 0.8979\nEpoch 00028: early stopping\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = keras.models.load_model('dsb.h5', custom_objects={'dice_coef_loss': dice_coef_loss,'dice_coef':dice_coef})\ntest_mask = model.predict(x_test, verbose=1)","execution_count":46,"outputs":[{"output_type":"stream","text":"65/65 [==============================] - 1s 21ms/sample\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_mask[0].shape","execution_count":65,"outputs":[{"output_type":"execute_result","execution_count":65,"data":{"text/plain":"(256, 256, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds_test_t = (test_mask > 0.5).astype(np.uint8)","execution_count":47,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"r = random.randint(0, len(x_test)-1)\n\nfig = plt.figure()\nfig.subplots_adjust(hspace=0.4, wspace=0.4)\nax = fig.add_subplot(1, 2, 1)\nax.imshow(x_test[r])\nax = fig.add_subplot(1, 2, 2)\nax.imshow(test_mask[r][:,:,0])","execution_count":69,"outputs":[{"output_type":"execute_result","execution_count":69,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f138c8b2080>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from skimage.morphology import label\ndef rle_encoding(x):\n    dots = np.where(x.T.flatten() == 1)[0]\n    run_lengths = []\n    prev = -2\n    for b in dots:\n        if (b>prev+1): run_lengths.extend((b + 1, 0))\n        run_lengths[-1] += 1\n        prev = b\n    return run_lengths\n\ndef prob_to_rles(x, cutoff=0.5):\n    lab_img = label(x > cutoff)\n    for i in range(1, lab_img.max() + 1):\n        yield rle_encoding(lab_img == i)","execution_count":54,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from skimage.transform import resize\n\npreds_test_upsampled = []\nfor i in range(len(test_mask)):\n    preds_test_upsampled.append(resize(np.squeeze(test_mask[i]), \n                                       (test_sizes[i][0], test_sizes[i][1]), \n                                       mode='constant', preserve_range=True))","execution_count":50,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds_test_upsampled[0].shape","execution_count":71,"outputs":[{"output_type":"execute_result","execution_count":71,"data":{"text/plain":"(256, 256)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_test_ids = []\nrles = []\nfor n, id_ in enumerate(test_ids):\n    rle = list(prob_to_rles(preds_test_upsampled[n]))\n    rles.append(rle)\n    new_test_ids.append([id_] * len(rle))","execution_count":74,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.DataFrame()\nsub['ImageId'] = new_test_ids\nsub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x))\nsub.to_csv('dsb2k18_01.csv', index=False)","execution_count":78,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}