keras-u-net-starter-v4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7579927\n",
      "3.7836285\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['PYTHONHASHSEED'] = '0'\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "tf.reset_default_graph()\n",
    "\n",
    "random_seed = 42\n",
    "from numpy.random import seed\n",
    "seed(random_seed)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(random_seed)\n",
    "import random\n",
    "random.seed(random_seed)\n",
    "#print(random.getstate())\n",
    "\n",
    "from keras import backend as K\n",
    "\n",
    "with tf.device('/gpu:0'):\n",
    "    c = tf.random_uniform([], -10, 10)\n",
    "    d = tf.random_uniform([], -10, 10)\n",
    "\n",
    "with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:\n",
    "    print(sess.run(c))\n",
    "    print(sess.run(d))\n",
    "    \n",
    "    \n",
    "session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)   \n",
    "sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)    \n",
    "\n",
    "K.set_session(sess)    \n",
    "#print(sess)\n",
    "#config = tf.ConfigProto(\n",
    "#        device_count = {'GPU': 0}\n",
    "#    )\n",
    "#sess = tf.Session(config=config)\n",
    "# https://machinelearningmastery.com/reproducible-results-neural-networks-keras/\n",
    "# https://stackoverflow.com/questions/45230448/how-to-get-reproducible-result-when-running-keras-with-tensorflow-backend\n",
    "# https://stackoverflow.com/questions/46836857/results-not-reproducible-with-keras-and-tensorflow-in-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input\n",
    "from keras.layers.core import Lambda\n",
    "from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.optimizers import SGD, RMSprop, Adam\n",
    "\n",
    "import keras\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set some parameters\n",
    "IMG_WIDTH = 256\n",
    "IMG_HEIGHT = 256\n",
    "IMG_CHANNELS = 3\n",
    "TRAIN_PATH = 'data/stage1_train/'\n",
    "TEST_PATH = 'data/stage1_test/'\n",
    "DATA_PATH = 'data/'\n",
    "\n",
    "warnings.filterwarnings('ignore', category=UserWarning, module='skimage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "670\n",
      "65\n"
     ]
    }
   ],
   "source": [
    "# Get train and test IDs\n",
    "train_ids = next(os.walk(TRAIN_PATH))[1]\n",
    "test_ids = next(os.walk(TEST_PATH))[1]\n",
    "print(len(train_ids))\n",
    "print(len(test_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/chen/workspace/git_examples/kaggle-nuclei-challenge\n"
     ]
    }
   ],
   "source": [
    "current_dir = os.getcwd()\n",
    "print(current_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bcolz\n",
    "def save_array(fname, arr): c=bcolz.carray(arr, rootdir=fname, mode='w'); c.flush()\n",
    "def load_array(fname): return bcolz.open(fname)[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def fit_model():\n",
    "    model = get_model_bn_do()\n",
    "    model.fit_generator(batches, batches.N, nb_epoch=1, verbose=0,\n",
    "                        validation_data=test_batches, nb_val_samples=test_batches.N)\n",
    "    model.optimizer.lr=0.1\n",
    "    model.fit_generator(batches, batches.N, nb_epoch=4, verbose=0,\n",
    "                        validation_data=test_batches, nb_val_samples=test_batches.N)\n",
    "    model.optimizer.lr=0.01\n",
    "    model.fit_generator(batches, batches.N, nb_epoch=12, verbose=0,\n",
    "                        validation_data=test_batches, nb_val_samples=test_batches.N)\n",
    "    model.optimizer.lr=0.001\n",
    "    model.fit_generator(batches, batches.N, nb_epoch=18, verbose=0,\n",
    "                        validation_data=test_batches, nb_val_samples=test_batches.N)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing train images and masks ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 670/670 [01:22<00:00,  8.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 65/65 [00:00<00:00, 96.68it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Get and resize train images and masks\n",
    "X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
    "Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
    "print('Getting and resizing train images and masks ... ')\n",
    "sys.stdout.flush()\n",
    "\n",
    "filenames = []\n",
    "for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
    "    path = TRAIN_PATH + id_\n",
    "    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
    "    filenames.append(path + '/images/' + id_ + '.png')\n",
    "    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
    "    X_train[n] = img\n",
    "    mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
    "    for mask_file in next(os.walk(path + '/masks/'))[2]:\n",
    "        mask_ = imread(path + '/masks/' + mask_file)\n",
    "        mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', \n",
    "                                      preserve_range=True), axis=-1)\n",
    "        mask = np.maximum(mask, mask_)\n",
    "    Y_train[n] = mask\n",
    "\n",
    "# Get and resize test images\n",
    "X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
    "sizes_test = []\n",
    "print('Getting and resizing test images ... ')\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n",
    "    path = TEST_PATH + id_\n",
    "    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
    "    sizes_test.append([img.shape[0], img.shape[1]])\n",
    "    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
    "    X_test[n] = img\n",
    "\n",
    "save_array(DATA_PATH + 'X_train_256.bc', X_train)\n",
    "save_array(DATA_PATH + 'Y_train_256.bc', Y_train)\n",
    "save_array(DATA_PATH + 'X_test_256.bc', X_test)\n",
    "save_array(DATA_PATH + 'filenames_256.bc', filenames)\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = load_array(DATA_PATH + 'X_train_256.bc')\n",
    "Y_train = load_array(DATA_PATH + 'Y_train_256.bc')\n",
    "X_test = load_array(DATA_PATH + 'X_test_256.bc')\n",
    "filenames = load_array(DATA_PATH + 'filenames_256.bc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({(256, 256): 334,\n",
       "         (320, 256): 112,\n",
       "         (347, 260): 5,\n",
       "         (360, 360): 91,\n",
       "         (640, 512): 13,\n",
       "         (696, 520): 92,\n",
       "         (1024, 1024): 16,\n",
       "         (1272, 603): 6,\n",
       "         (1388, 1040): 1})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PIL\n",
    "import collections\n",
    "sizes = [PIL.Image.open(f).size for f in filenames]\n",
    "collections.Counter(sizes)\n",
    "collections.Counter(sizes_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n"
     ]
    },
    {
     "data": {
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+6i8lw2yA9xxdZw2rrmcB742XOW5lImveGSf3Ai9TAvaOzcaMmKO85mfNP2Nr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\ndGTEylLRmjHWlwE6a2zWzgJBpi+TZDIMowVKu7BrMVsv8DyxkpG3tx7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd42d95a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CvdgG69Q/zKa3Qesg6uskagX8KMmxJPu7tm1VdaZbfh3Y1qa0kQ2rt0/bpHcT8503uWDv\ntsGsJkdsHUR9dUNV7QZuBu5K8qnBJ2ulf9qb8yL6Vm9noon5WlhjcsH39WEbTHtyxEGtg2ikSdTm\nTVWd7u7PAo+w0u18Y7X73N2fbVfhSIbV24ttUhNOzLfV1ppckB5tg1lMjjiodRA9BexKcnWSC1m5\n+sfhxjWtK8nF3dVMSHIx8GlWJoU7DOzrVtsHPNqmwpENq/cwcEd35OZ64K2B3Ye50aeJ+YZNLkhP\ntsGw+qe6DVqOxg8cIXiJlZH1r7WuZ4R6P8rKEYGfASdWawZ+HzgCvAz8GLi8da0DNT/IStf5/1jZ\nX79zWL2sHKn5x257PAvsmdP6/7mr73j3i799YP2vdfW/CNw8B/XfwMpu13Hgme52S1+2wTr1T20b\n+BEPSc213jWTJINIUnsGkaTmDCJJzRlEkpoziCQ1ZxBJau7/AR6INI1Wi6wKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd47fcef790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if training data looks all right\n",
    "ix = np.random.randint(0, len(train_ids))\n",
    "print(ix)\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define IoU metric\n",
    "def mean_iou(y_true, y_pred):\n",
    "    prec = []\n",
    "    for t in np.arange(0.5, 1.0, 0.05):\n",
    "        y_pred_ = tf.to_int32(y_pred > t)\n",
    "        score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)\n",
    "        K.get_session().run(tf.local_variables_initializer())\n",
    "        with tf.control_dependencies([up_opt]):\n",
    "            score = tf.identity(score)\n",
    "        prec.append(score)\n",
    "    return K.mean(K.stack(prec), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build U-Net model\n",
    "inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
    "s = Lambda(lambda x: x / 255) (inputs)\n",
    "\n",
    "c1 = Conv2D(16, (3, 3), activation='relu', padding='same') (s)\n",
    "c1 = Conv2D(16, (3, 3), activation='relu', padding='same') (c1)\n",
    "p1 = MaxPooling2D((2, 2)) (c1)\n",
    "\n",
    "c2 = Conv2D(32, (3, 3), activation='relu', padding='same') (p1)\n",
    "c2 = Conv2D(32, (3, 3), activation='relu', padding='same') (c2)\n",
    "p2 = MaxPooling2D((2, 2)) (c2)\n",
    "\n",
    "c3 = Conv2D(64, (3, 3), activation='relu', padding='same') (p2)\n",
    "c3 = Conv2D(64, (3, 3), activation='relu', padding='same') (c3)\n",
    "p3 = MaxPooling2D((2, 2)) (c3)\n",
    "\n",
    "c4 = Conv2D(128, (3, 3), activation='relu', padding='same') (p3)\n",
    "c4 = Conv2D(128, (3, 3), activation='relu', padding='same') (c4)\n",
    "p4 = MaxPooling2D(pool_size=(2, 2)) (c4)\n",
    "\n",
    "c5 = Conv2D(256, (3, 3), activation='relu', padding='same') (p4)\n",
    "c5 = Conv2D(256, (3, 3), activation='relu', padding='same') (c5)\n",
    "\n",
    "u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5)\n",
    "u6 = concatenate([u6, c4])\n",
    "c6 = Conv2D(128, (3, 3), activation='relu', padding='same') (u6)\n",
    "c6 = Conv2D(128, (3, 3), activation='relu', padding='same') (c6)\n",
    "\n",
    "u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6)\n",
    "u7 = concatenate([u7, c3])\n",
    "c7 = Conv2D(64, (3, 3), activation='relu', padding='same') (u7)\n",
    "c7 = Conv2D(64, (3, 3), activation='relu', padding='same') (c7)\n",
    "\n",
    "u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7)\n",
    "u8 = concatenate([u8, c2])\n",
    "c8 = Conv2D(32, (3, 3), activation='relu', padding='same') (u8)\n",
    "c8 = Conv2D(32, (3, 3), activation='relu', padding='same') (c8)\n",
    "\n",
    "u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8)\n",
    "u9 = concatenate([u9, c1], axis=3)\n",
    "c9 = Conv2D(16, (3, 3), activation='relu', padding='same') (u9)\n",
    "c9 = Conv2D(16, (3, 3), activation='relu', padding='same') (c9)\n",
    "\n",
    "outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)\n",
    "\n",
    "model = Model(inputs=[inputs], outputs=[outputs])\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[mean_iou])\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.4321 - mean_iou: 0.4164\n",
      "Epoch 00001: val_loss improved from inf to 0.30474, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 8s 14ms/step - loss: 0.4316 - mean_iou: 0.4165 - val_loss: 0.3047 - val_mean_iou: 0.4245\n",
      "Epoch 2/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.2336 - mean_iou: 0.4402\n",
      "Epoch 00002: val_loss improved from 0.30474 to 0.22068, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.2329 - mean_iou: 0.4404 - val_loss: 0.2207 - val_mean_iou: 0.4822\n",
      "Epoch 3/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1652 - mean_iou: 0.5146\n",
      "Epoch 00003: val_loss improved from 0.22068 to 0.15799, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.1651 - mean_iou: 0.5148 - val_loss: 0.1580 - val_mean_iou: 0.5514\n",
      "Epoch 4/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1166 - mean_iou: 0.5834\n",
      "Epoch 00004: val_loss improved from 0.15799 to 0.13586, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.1171 - mean_iou: 0.5835 - val_loss: 0.1359 - val_mean_iou: 0.6094\n",
      "Epoch 5/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1021 - mean_iou: 0.6278\n",
      "Epoch 00005: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.1023 - mean_iou: 0.6279 - val_loss: 0.1444 - val_mean_iou: 0.6460\n",
      "Epoch 6/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0882 - mean_iou: 0.6601\n",
      "Epoch 00006: val_loss improved from 0.13586 to 0.10718, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0881 - mean_iou: 0.6602 - val_loss: 0.1072 - val_mean_iou: 0.6749\n",
      "Epoch 7/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0852 - mean_iou: 0.6867\n",
      "Epoch 00007: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0849 - mean_iou: 0.6868 - val_loss: 0.1129 - val_mean_iou: 0.6975\n",
      "Epoch 8/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0837 - mean_iou: 0.7064\n",
      "Epoch 00008: val_loss improved from 0.10718 to 0.10044, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0837 - mean_iou: 0.7065 - val_loss: 0.1004 - val_mean_iou: 0.7145\n",
      "Epoch 9/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0772 - mean_iou: 0.7218\n",
      "Epoch 00009: val_loss improved from 0.10044 to 0.09710, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0770 - mean_iou: 0.7218 - val_loss: 0.0971 - val_mean_iou: 0.7289\n",
      "Epoch 10/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0774 - mean_iou: 0.7350\n",
      "Epoch 00010: val_loss improved from 0.09710 to 0.09369, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0773 - mean_iou: 0.7350 - val_loss: 0.0937 - val_mean_iou: 0.7408\n",
      "Epoch 11/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0733 - mean_iou: 0.7462\n",
      "Epoch 00011: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0734 - mean_iou: 0.7462 - val_loss: 0.0941 - val_mean_iou: 0.7510\n",
      "Epoch 12/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0725 - mean_iou: 0.7553\n",
      "Epoch 00012: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0723 - mean_iou: 0.7554 - val_loss: 0.0958 - val_mean_iou: 0.7596\n",
      "Epoch 13/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0736 - mean_iou: 0.7633\n",
      "Epoch 00013: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0734 - mean_iou: 0.7633 - val_loss: 0.0946 - val_mean_iou: 0.7668\n",
      "Epoch 14/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0736 - mean_iou: 0.7703\n",
      "Epoch 00014: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0739 - mean_iou: 0.7703 - val_loss: 0.0946 - val_mean_iou: 0.7733\n",
      "Epoch 15/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0700 - mean_iou: 0.7763\n",
      "Epoch 00015: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0702 - mean_iou: 0.7763 - val_loss: 0.0966 - val_mean_iou: 0.7791\n",
      "Epoch 16/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0695 - mean_iou: 0.7817\n",
      "Epoch 00016: val_loss improved from 0.09369 to 0.08732, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0693 - mean_iou: 0.7817 - val_loss: 0.0873 - val_mean_iou: 0.7842\n",
      "Epoch 17/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0668 - mean_iou: 0.7864\n",
      "Epoch 00017: val_loss improved from 0.08732 to 0.08695, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0668 - mean_iou: 0.7864 - val_loss: 0.0870 - val_mean_iou: 0.7889\n",
      "Epoch 18/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0681 - mean_iou: 0.7911\n",
      "Epoch 00018: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0681 - mean_iou: 0.7911 - val_loss: 0.0950 - val_mean_iou: 0.7930\n",
      "Epoch 19/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0675 - mean_iou: 0.7946\n",
      "Epoch 00019: val_loss improved from 0.08695 to 0.08454, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0675 - mean_iou: 0.7946 - val_loss: 0.0845 - val_mean_iou: 0.7966\n",
      "Epoch 20/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0678 - mean_iou: 0.7982\n",
      "Epoch 00020: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0677 - mean_iou: 0.7982 - val_loss: 0.0879 - val_mean_iou: 0.8000\n",
      "Epoch 21/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0659 - mean_iou: 0.8017\n",
      "Epoch 00021: val_loss improved from 0.08454 to 0.08437, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0659 - mean_iou: 0.8017 - val_loss: 0.0844 - val_mean_iou: 0.8034\n",
      "Epoch 22/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0669 - mean_iou: 0.8049\n",
      "Epoch 00022: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0668 - mean_iou: 0.8049 - val_loss: 0.0888 - val_mean_iou: 0.8063\n",
      "Epoch 23/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0665 - mean_iou: 0.8075\n",
      "Epoch 00023: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0665 - mean_iou: 0.8075 - val_loss: 0.0853 - val_mean_iou: 0.8089\n",
      "Epoch 24/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0663 - mean_iou: 0.8101\n",
      "Epoch 00024: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0662 - mean_iou: 0.8102 - val_loss: 0.0860 - val_mean_iou: 0.8113\n",
      "Epoch 25/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0674 - mean_iou: 0.8123\n",
      "Epoch 00025: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0672 - mean_iou: 0.8123 - val_loss: 0.0857 - val_mean_iou: 0.8134\n",
      "Epoch 26/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0657 - mean_iou: 0.8145\n",
      "Epoch 00026: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0657 - mean_iou: 0.8145 - val_loss: 0.0919 - val_mean_iou: 0.8155\n",
      "Epoch 27/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0655 - mean_iou: 0.8163\n",
      "Epoch 00027: val_loss improved from 0.08437 to 0.08083, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0657 - mean_iou: 0.8163 - val_loss: 0.0808 - val_mean_iou: 0.8174\n",
      "Epoch 28/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0650 - mean_iou: 0.8183\n",
      "Epoch 00028: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0655 - mean_iou: 0.8183 - val_loss: 0.0939 - val_mean_iou: 0.8192\n",
      "Epoch 29/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/603 [============================>.] - ETA: 0s - loss: 0.0641 - mean_iou: 0.8201\n",
      "Epoch 00029: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0641 - mean_iou: 0.8201 - val_loss: 0.0832 - val_mean_iou: 0.8210\n",
      "Epoch 30/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0632 - mean_iou: 0.8218\n",
      "Epoch 00030: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0631 - mean_iou: 0.8218 - val_loss: 0.0843 - val_mean_iou: 0.8226\n",
      "Epoch 31/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0629 - mean_iou: 0.8233\n",
      "Epoch 00031: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0627 - mean_iou: 0.8233 - val_loss: 0.0818 - val_mean_iou: 0.8242\n",
      "Epoch 32/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0619 - mean_iou: 0.8249\n",
      "Epoch 00032: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0617 - mean_iou: 0.8249 - val_loss: 0.0828 - val_mean_iou: 0.8257\n",
      "Epoch 33/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0602 - mean_iou: 0.8265\n",
      "Epoch 00033: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0605 - mean_iou: 0.8265 - val_loss: 0.0860 - val_mean_iou: 0.8273\n",
      "Epoch 34/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0596 - mean_iou: 0.8280\n",
      "Epoch 00034: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0598 - mean_iou: 0.8280 - val_loss: 0.0847 - val_mean_iou: 0.8288\n",
      "Epoch 35/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0657 - mean_iou: 0.8293\n",
      "Epoch 00035: val_loss improved from 0.08083 to 0.08019, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0657 - mean_iou: 0.8293 - val_loss: 0.0802 - val_mean_iou: 0.8299\n",
      "Epoch 36/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0606 - mean_iou: 0.8304\n",
      "Epoch 00036: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0610 - mean_iou: 0.8305 - val_loss: 0.0815 - val_mean_iou: 0.8312\n",
      "Epoch 37/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0612 - mean_iou: 0.8316\n",
      "Epoch 00037: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0611 - mean_iou: 0.8317 - val_loss: 0.0892 - val_mean_iou: 0.8323\n",
      "Epoch 38/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0626 - mean_iou: 0.8328\n",
      "Epoch 00038: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0625 - mean_iou: 0.8328 - val_loss: 0.0917 - val_mean_iou: 0.8333\n",
      "Epoch 39/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0637 - mean_iou: 0.8337\n",
      "Epoch 00039: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0636 - mean_iou: 0.8337 - val_loss: 0.0804 - val_mean_iou: 0.8343\n",
      "Epoch 40/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0614 - mean_iou: 0.8348\n",
      "Epoch 00040: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0613 - mean_iou: 0.8348 - val_loss: 0.0813 - val_mean_iou: 0.8353\n",
      "Epoch 41/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0601 - mean_iou: 0.8358\n",
      "Epoch 00041: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0601 - mean_iou: 0.8358 - val_loss: 0.0834 - val_mean_iou: 0.8363\n",
      "Epoch 42/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0599 - mean_iou: 0.8367\n",
      "Epoch 00042: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0597 - mean_iou: 0.8367 - val_loss: 0.0823 - val_mean_iou: 0.8372\n",
      "Epoch 43/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0577 - mean_iou: 0.8377\n",
      "Epoch 00043: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0580 - mean_iou: 0.8377 - val_loss: 0.0911 - val_mean_iou: 0.8382\n",
      "Epoch 44/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0585 - mean_iou: 0.8387\n",
      "Epoch 00044: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0586 - mean_iou: 0.8387 - val_loss: 0.0805 - val_mean_iou: 0.8392\n",
      "Epoch 45/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0581 - mean_iou: 0.8396\n",
      "Epoch 00045: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0581 - mean_iou: 0.8396 - val_loss: 0.0866 - val_mean_iou: 0.8400\n",
      "Epoch 46/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0595 - mean_iou: 0.8404\n",
      "Epoch 00046: val_loss improved from 0.08019 to 0.07958, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0595 - mean_iou: 0.8404 - val_loss: 0.0796 - val_mean_iou: 0.8408\n",
      "Epoch 47/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0574 - mean_iou: 0.8413\n",
      "Epoch 00047: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0572 - mean_iou: 0.8413 - val_loss: 0.0887 - val_mean_iou: 0.8417\n",
      "Epoch 48/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0560 - mean_iou: 0.8420\n",
      "Epoch 00048: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0560 - mean_iou: 0.8420 - val_loss: 0.0844 - val_mean_iou: 0.8425\n",
      "Epoch 49/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0536 - mean_iou: 0.8430\n",
      "Epoch 00049: val_loss improved from 0.07958 to 0.07914, saving model to model-tmp-v4.h5\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0537 - mean_iou: 0.8430 - val_loss: 0.0791 - val_mean_iou: 0.8434\n",
      "Epoch 50/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0520 - mean_iou: 0.8439\n",
      "Epoch 00050: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0520 - mean_iou: 0.8439 - val_loss: 0.0862 - val_mean_iou: 0.8444\n",
      "Epoch 51/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0511 - mean_iou: 0.8448\n",
      "Epoch 00051: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0512 - mean_iou: 0.8448 - val_loss: 0.0822 - val_mean_iou: 0.8452\n",
      "Epoch 52/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0507 - mean_iou: 0.8457\n",
      "Epoch 00052: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0506 - mean_iou: 0.8457 - val_loss: 0.0868 - val_mean_iou: 0.8462\n",
      "Epoch 53/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0492 - mean_iou: 0.8466\n",
      "Epoch 00053: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0493 - mean_iou: 0.8466 - val_loss: 0.0822 - val_mean_iou: 0.8471\n",
      "Epoch 54/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0488 - mean_iou: 0.8475\n",
      "Epoch 00054: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0487 - mean_iou: 0.8475 - val_loss: 0.0849 - val_mean_iou: 0.8480\n",
      "Epoch 55/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0471 - mean_iou: 0.8485\n",
      "Epoch 00055: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0473 - mean_iou: 0.8485 - val_loss: 0.0883 - val_mean_iou: 0.8489\n",
      "Epoch 56/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0475 - mean_iou: 0.8493\n",
      "Epoch 00056: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0478 - mean_iou: 0.8493 - val_loss: 0.0846 - val_mean_iou: 0.8498\n",
      "Epoch 57/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0448 - mean_iou: 0.8503\n",
      "Epoch 00057: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0448 - mean_iou: 0.8503 - val_loss: 0.0858 - val_mean_iou: 0.8508\n",
      "Epoch 58/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0447 - mean_iou: 0.8512\n",
      "Epoch 00058: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0447 - mean_iou: 0.8512 - val_loss: 0.0889 - val_mean_iou: 0.8516\n",
      "Epoch 59/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/603 [============================>.] - ETA: 0s - loss: 0.0442 - mean_iou: 0.8521\n",
      "Epoch 00059: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0441 - mean_iou: 0.8521 - val_loss: 0.0879 - val_mean_iou: 0.8525\n",
      "Epoch 60/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0417 - mean_iou: 0.8530\n",
      "Epoch 00060: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0417 - mean_iou: 0.8530 - val_loss: 0.0944 - val_mean_iou: 0.8534\n",
      "Epoch 61/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0411 - mean_iou: 0.8539\n",
      "Epoch 00061: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0410 - mean_iou: 0.8539 - val_loss: 0.0876 - val_mean_iou: 0.8544\n",
      "Epoch 62/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0435 - mean_iou: 0.8548\n",
      "Epoch 00062: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0434 - mean_iou: 0.8548 - val_loss: 0.0946 - val_mean_iou: 0.8552\n",
      "Epoch 63/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0398 - mean_iou: 0.8556\n",
      "Epoch 00063: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0398 - mean_iou: 0.8556 - val_loss: 0.0973 - val_mean_iou: 0.8561\n",
      "Epoch 64/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0375 - mean_iou: 0.8566\n",
      "Epoch 00064: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0378 - mean_iou: 0.8566 - val_loss: 0.0922 - val_mean_iou: 0.8570\n",
      "Epoch 65/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0375 - mean_iou: 0.8575\n",
      "Epoch 00065: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0375 - mean_iou: 0.8575 - val_loss: 0.1081 - val_mean_iou: 0.8579\n",
      "Epoch 66/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0387 - mean_iou: 0.8583\n",
      "Epoch 00066: val_loss did not improve\n",
      "603/603 [==============================] - 7s 12ms/step - loss: 0.0386 - mean_iou: 0.8583 - val_loss: 0.0959 - val_mean_iou: 0.8588\n",
      "Epoch 67/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0348 - mean_iou: 0.8592\n",
      "Epoch 00067: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0351 - mean_iou: 0.8592 - val_loss: 0.1011 - val_mean_iou: 0.8597\n",
      "Epoch 68/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0347 - mean_iou: 0.8601\n",
      "Epoch 00068: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0347 - mean_iou: 0.8601 - val_loss: 0.0986 - val_mean_iou: 0.8606\n",
      "Epoch 69/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0342 - mean_iou: 0.8610\n",
      "Epoch 00069: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0343 - mean_iou: 0.8610 - val_loss: 0.1034 - val_mean_iou: 0.8615\n",
      "Epoch 70/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0346 - mean_iou: 0.8619\n",
      "Epoch 00070: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0345 - mean_iou: 0.8619 - val_loss: 0.1032 - val_mean_iou: 0.8623\n",
      "Epoch 71/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0335 - mean_iou: 0.8627\n",
      "Epoch 00071: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0333 - mean_iou: 0.8627 - val_loss: 0.1048 - val_mean_iou: 0.8632\n",
      "Epoch 72/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0309 - mean_iou: 0.8636\n",
      "Epoch 00072: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0310 - mean_iou: 0.8636 - val_loss: 0.1126 - val_mean_iou: 0.8641\n",
      "Epoch 73/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0314 - mean_iou: 0.8645\n",
      "Epoch 00073: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0314 - mean_iou: 0.8645 - val_loss: 0.1087 - val_mean_iou: 0.8650\n",
      "Epoch 74/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0301 - mean_iou: 0.8654\n",
      "Epoch 00074: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0301 - mean_iou: 0.8654 - val_loss: 0.1104 - val_mean_iou: 0.8658\n",
      "Epoch 75/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0288 - mean_iou: 0.8663\n",
      "Epoch 00075: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0288 - mean_iou: 0.8663 - val_loss: 0.1142 - val_mean_iou: 0.8667\n",
      "Epoch 76/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0275 - mean_iou: 0.8671\n",
      "Epoch 00076: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0275 - mean_iou: 0.8671 - val_loss: 0.1184 - val_mean_iou: 0.8676\n",
      "Epoch 77/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0271 - mean_iou: 0.8680\n",
      "Epoch 00077: val_loss did not improve\n",
      "603/603 [==============================] - 6s 11ms/step - loss: 0.0270 - mean_iou: 0.8680 - val_loss: 0.1238 - val_mean_iou: 0.8685\n",
      "Epoch 78/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0287 - mean_iou: 0.8689\n",
      "Epoch 00078: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0288 - mean_iou: 0.8689 - val_loss: 0.1166 - val_mean_iou: 0.8693\n",
      "Epoch 79/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0285 - mean_iou: 0.8697\n",
      "Epoch 00079: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0285 - mean_iou: 0.8697 - val_loss: 0.1073 - val_mean_iou: 0.8701\n",
      "Epoch 80/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0282 - mean_iou: 0.8705\n",
      "Epoch 00080: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0282 - mean_iou: 0.8705 - val_loss: 0.1167 - val_mean_iou: 0.8709\n",
      "Epoch 81/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0263 - mean_iou: 0.8713\n",
      "Epoch 00081: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0263 - mean_iou: 0.8713 - val_loss: 0.1220 - val_mean_iou: 0.8717\n",
      "Epoch 82/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0247 - mean_iou: 0.8721\n",
      "Epoch 00082: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0246 - mean_iou: 0.8721 - val_loss: 0.1263 - val_mean_iou: 0.8725\n",
      "Epoch 83/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0238 - mean_iou: 0.8729\n",
      "Epoch 00083: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0238 - mean_iou: 0.8729 - val_loss: 0.1291 - val_mean_iou: 0.8733\n",
      "Epoch 84/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0238 - mean_iou: 0.8737\n",
      "Epoch 00084: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0238 - mean_iou: 0.8737 - val_loss: 0.1350 - val_mean_iou: 0.8741\n",
      "Epoch 85/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0231 - mean_iou: 0.8745\n",
      "Epoch 00085: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0232 - mean_iou: 0.8745 - val_loss: 0.1324 - val_mean_iou: 0.8749\n",
      "Epoch 86/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0234 - mean_iou: 0.8753\n",
      "Epoch 00086: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0234 - mean_iou: 0.8753 - val_loss: 0.1363 - val_mean_iou: 0.8757\n",
      "Epoch 87/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0227 - mean_iou: 0.8761\n",
      "Epoch 00087: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0227 - mean_iou: 0.8761 - val_loss: 0.1310 - val_mean_iou: 0.8765\n",
      "Epoch 88/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0231 - mean_iou: 0.8768\n",
      "Epoch 00088: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0230 - mean_iou: 0.8768 - val_loss: 0.1299 - val_mean_iou: 0.8772\n",
      "Epoch 89/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0221 - mean_iou: 0.8776\n",
      "Epoch 00089: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0220 - mean_iou: 0.8776 - val_loss: 0.1348 - val_mean_iou: 0.8780\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 90/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0212 - mean_iou: 0.8783\n",
      "Epoch 00090: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0212 - mean_iou: 0.8783 - val_loss: 0.1364 - val_mean_iou: 0.8787\n",
      "Epoch 91/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0204 - mean_iou: 0.8791\n",
      "Epoch 00091: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0204 - mean_iou: 0.8791 - val_loss: 0.1488 - val_mean_iou: 0.8795\n",
      "Epoch 92/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0208 - mean_iou: 0.8798\n",
      "Epoch 00092: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0209 - mean_iou: 0.8798 - val_loss: 0.1416 - val_mean_iou: 0.8802\n",
      "Epoch 93/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0217 - mean_iou: 0.8805\n",
      "Epoch 00093: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0216 - mean_iou: 0.8805 - val_loss: 0.1445 - val_mean_iou: 0.8809\n",
      "Epoch 94/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0204 - mean_iou: 0.8812\n",
      "Epoch 00094: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0204 - mean_iou: 0.8812 - val_loss: 0.1521 - val_mean_iou: 0.8816\n",
      "Epoch 95/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0193 - mean_iou: 0.8820\n",
      "Epoch 00095: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0193 - mean_iou: 0.8820 - val_loss: 0.1482 - val_mean_iou: 0.8823\n",
      "Epoch 96/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0202 - mean_iou: 0.8827\n",
      "Epoch 00096: val_loss did not improve\n",
      "603/603 [==============================] - 7s 11ms/step - loss: 0.0202 - mean_iou: 0.8827 - val_loss: 0.1350 - val_mean_iou: 0.8830\n",
      "Epoch 97/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0210 - mean_iou: 0.8833\n",
      "Epoch 00097: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0210 - mean_iou: 0.8833 - val_loss: 0.1375 - val_mean_iou: 0.8837\n",
      "Epoch 98/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0200 - mean_iou: 0.8840\n",
      "Epoch 00098: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0200 - mean_iou: 0.8840 - val_loss: 0.1422 - val_mean_iou: 0.8843\n",
      "Epoch 99/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0189 - mean_iou: 0.8846\n",
      "Epoch 00099: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0189 - mean_iou: 0.8846 - val_loss: 0.1443 - val_mean_iou: 0.8850\n",
      "Epoch 100/100\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0193 - mean_iou: 0.8853\n",
      "Epoch 00100: val_loss did not improve\n",
      "603/603 [==============================] - 6s 9ms/step - loss: 0.0192 - mean_iou: 0.8853 - val_loss: 0.1502 - val_mean_iou: 0.8856\n"
     ]
    }
   ],
   "source": [
    "#earlystopper = EarlyStopping(patience=5, verbose=1)\n",
    "checkpointer = ModelCheckpoint('model-tmp-v4.h5', verbose=1, save_best_only=True)\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=100, verbose=1, callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00001: val_loss did not improve\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "\n",
      "Epoch 00003: val_loss did not improve\n",
      "\n",
      "Epoch 00004: val_loss did not improve\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      "\n",
      "Epoch 00016: val_loss did not improve\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      "\n",
      "Epoch 00022: val_loss did not improve\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/1\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0135 - mean_iou: 0.9014\n",
      "Epoch 00001: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0135 - mean_iou: 0.9014 - val_loss: 0.1763 - val_mean_iou: 0.9016\n"
     ]
    }
   ],
   "source": [
    "K.set_value(model.optimizer.lr, 0.001)\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=30, verbose=0, callbacks=[checkpointer])\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=1, verbose=1, callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00001: val_loss did not improve\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "\n",
      "Epoch 00003: val_loss did not improve\n",
      "\n",
      "Epoch 00004: val_loss did not improve\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      "\n",
      "Epoch 00016: val_loss did not improve\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      "\n",
      "Epoch 00022: val_loss did not improve\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/1\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0062 - mean_iou: 0.9147\n",
      "Epoch 00001: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0062 - mean_iou: 0.9147 - val_loss: 0.2918 - val_mean_iou: 0.9149\n"
     ]
    }
   ],
   "source": [
    "K.set_value(model.optimizer.lr, 0.0001)\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=30, verbose=0, callbacks=[checkpointer])\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=1, verbose=1, callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00001: val_loss did not improve\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "\n",
      "Epoch 00003: val_loss did not improve\n",
      "\n",
      "Epoch 00004: val_loss did not improve\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      "\n",
      "Epoch 00016: val_loss did not improve\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      "\n",
      "Epoch 00022: val_loss did not improve\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/1\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0055 - mean_iou: 0.9245\n",
      "Epoch 00001: val_loss did not improve\n",
      "603/603 [==============================] - 6s 10ms/step - loss: 0.0055 - mean_iou: 0.9245 - val_loss: 0.3102 - val_mean_iou: 0.9246\n"
     ]
    }
   ],
   "source": [
    "K.set_value(model.optimizer.lr, 0.00001)\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=30, verbose=0, callbacks=[checkpointer])\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=1, verbose=1, callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 1s 2ms/step\n",
      "67/67 [==============================] - 0s 2ms/step\n",
      "65/65 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "model.save_weights(DATA_PATH+'model-tmp-v4_last.h5')\n",
    "# Predict on train, val and test\n",
    "#model = load_model('model-tmp-v4.h5', custom_objects={'mean_iou': mean_iou})\n",
    "\n",
    "preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)\n",
    "preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)\n",
    "preds_test = model.predict(X_test, verbose=1)\n",
    "\n",
    "# Threshold predictions\n",
    "preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
    "preds_val_t = (preds_val > 0.5).astype(np.uint8)\n",
    "preds_test_t = (preds_test > 0.5).astype(np.uint8)\n",
    "\n",
    "# Create list of upsampled test masks\n",
    "preds_test_upsampled = []\n",
    "for i in range(len(preds_test)):\n",
    "    preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), \n",
    "                                       (sizes_test[i][0], sizes_test[i][1]), \n",
    "                                       mode='constant', preserve_range=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+v28gxL89jacrQuBSt4A/qoCeBagwEfS0XipQtO5++lhzf/T++XxurqbvOz5H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\nXY0XMTIPQnqflna7bcFpbiHDzGaucbty5QquXbuGnZ0dG08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y1nxme4gOvB0LtPXow29u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\nwAmC/DxpHpdCEEnd7mzPxfC6X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XdyNpsNDIlHtygA0a6emJgI07N6ZDPZU6vVwtraWqDJ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\nFDoY1QGp6zMsiCib4XXLWnhdNYX1vwDJ6XEVaO1Q3SgIdE0G6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suuwMBPApu2h7aL5qP/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3fbb6a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3fbbe0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fk91q/wr4LPCDKiedKCFKWl/69ZeBXg/0fmCnpAskXQ5sAr41yXvYbORevXNpt0P/7tkm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3fb786850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a sanity check on some random training samples\n",
    "ix = random.randint(0, len(preds_train_t))\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))\n",
    "plt.show()\n",
    "imshow(np.squeeze(preds_train_t[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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drSUQjYmILYaj39Xozsn5ejYwoiy3a+IGZ2uNQe+5MlvAmaXJHDDa4Jwmq6ty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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3fbb8f290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3fb593350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3fb541710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a sanity check on some random validation samples\n",
    "ix = random.randint(0, len(preds_val_t))\n",
    "imshow(X_train[int(X_train.shape[0]*0.9):][ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))\n",
    "plt.show()\n",
    "imshow(np.squeeze(preds_val_t[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run-length encoding stolen from https://www.kaggle.com/rakhlin/fast-run-length-encoding-python\n",
    "def 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",
    "\n",
    "def 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_test_ids = []\n",
    "rles = []\n",
    "for n, id_ in enumerate(test_ids):\n",
    "    rle = list(prob_to_rles(preds_test_upsampled[n]))\n",
    "    rles.extend(rle)\n",
    "    new_test_ids.extend([id_] * len(rle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create submission DataFrame\n",
    "sub = pd.DataFrame()\n",
    "sub['ImageId'] = new_test_ids\n",
    "sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x))\n",
    "sub.to_csv('sub-tmp-v4.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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