{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e61ef2d8-f315-4f7f-b07e-1de0f4e8441a",
    "_uuid": "1677fddbb95f7545b6540e9201f3339a0fdbfc5d"
   },
   "source": [
    "# Intro\n",
    "Hello! This rather quick and dirty kernel shows how to get started on segmenting nuclei using a neural network in Keras. \n",
    "\n",
    "The architecture used is the so-called [U-Net](https://arxiv.org/abs/1505.04597), which is very common for image segmentation problems such as this. I believe they also have a tendency to work quite well even on small datasets.\n",
    "\n",
    "Let's get started importing everything we need!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "c332549b-8d23-4bb5-8497-e7a8eb8b21d2",
    "_uuid": "5c38504af3a84bee68c66d3cde74443c58df422f",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "import numpy as np\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 import backend as K\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# Set some parameters\n",
    "IMG_WIDTH = 128\n",
    "IMG_HEIGHT = 128\n",
    "IMG_CHANNELS = 3\n",
    "TRAIN_PATH = '../input/stage1_train/'\n",
    "TEST_PATH = '../input/stage1_test/'\n",
    "\n",
    "warnings.filterwarnings('ignore', category=UserWarning, module='skimage')\n",
    "seed = 42\n",
    "random.seed = seed\n",
    "np.random.seed = seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "ffa0caf0-2d1b-40f2-865b-8e6db88526b6",
    "_uuid": "3fb9d6530fbbd0e22e41fc4fd9fd9fc0bff027ac",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get train and test IDs\n",
    "train_ids = next(os.walk(TRAIN_PATH))[1]\n",
    "test_ids = next(os.walk(TEST_PATH))[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "59c4a25d-645f-4b74-9c53-145ac78cc481",
    "_uuid": "875af74f980236825de3a650825b46e25632422c"
   },
   "source": [
    "# Get the data\n",
    "Let's first import all the images and associated masks. I downsample both the training and test images to keep things light and manageable, but we need to keep a record of the original sizes of the test images to upsample our predicted masks and create correct run-length encodings later on. There are definitely better ways to handle this, but it works fine for now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "ca0cc34b-c26f-41ee-88d7-975aebdb634e",
    "_uuid": "9e389ba8bdb5b6fc03b231b6a6c84a8bde634053",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing train images and masks ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 670/670 [01:25<00:00,  7.87it/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, 91.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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",
    "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",
    "    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",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c0523b03-1fc5-4505-a1b8-eb35ee617c8a",
    "_uuid": "d4f8327802a1ec6139ce0585953986272ba62ce1"
   },
   "source": [
    "Let's see if things look all right by drawing some random images and their associated masks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "88829b53-50ce-45d9-9540-77dd7384ad4c",
    "_uuid": "283af26f0860b7069bdfd133c746e5d20971542c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cjkHhTKgGKSNeaZBpmxGjicu9KcgA6L49c+YMgOWwDTIHTZ5OJhG7vlNufnWTp4zivC9l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\nwzEovBNyOByDwjshh8MxKP4/ViXg5LNiLK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2facfca7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1gZfV5Nhuqxz4vOr7O6f/t2wSem68mlV8P14s3wDOn7jfTuDZ8uWZWd+V7YT2A3uLn/cC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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2facfa25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if training data looks all right\n",
    "ix = random.randint(0, len(train_ids))\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2574ffe9-b911-4bfd-a00f-9ba5c25f45de",
    "_uuid": "938648da705689a0f940ff462477c801db3f0737"
   },
   "source": [
    "Seems good!\n",
    "\n",
    "# Create our Keras metric\n",
    "\n",
    "Now we try to define the *mean average precision at different intersection over union (IoU) thresholds* metric in Keras. TensorFlow has a mean IoU metric, but it doesn't have any native support for the mean over multiple thresholds, so I tried to implement this. **I'm by no means certain that this implementation is correct, though!** Any assistance in verifying this would be most welcome! \n",
    "\n",
    "*Update: This implementation is most definitely not correct due to the very large discrepancy between the results reported here and the LB results. It also seems to just increase over time no matter what when you train ... *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "c1df6f3a-d58f-434b-9216-ef7be38637d4",
    "_uuid": "5abd38950ae99b60f8afec7656eb654a48d449fe",
    "collapsed": true
   },
   "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": "markdown",
   "metadata": {
    "_cell_guid": "c3b9f148-1dba-4b6a-981b-6cdbf394fc3c",
    "_uuid": "986488a4c5223576be370e224426a30431911eb2"
   },
   "source": [
    "# Build and train our neural network\n",
    "Next we build our U-Net model, loosely based on [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) and very similar to [this repo](https://github.com/jocicmarko/ultrasound-nerve-segmentation) from the Kaggle Ultrasound Nerve Segmentation competition.\n",
    "\n",
    "![](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-architecture.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "c1dbc57c-b497-4ccb-b077-2053203ab7ed",
    "_uuid": "0aa97d66c29f45dfac9b0f45fcf74ba0e778ba5d",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 128, 128, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 128, 128, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 128, 128, 8)  224         lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 128, 128, 8)  584         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 64, 64, 8)    0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 64, 64, 16)   1168        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 64, 64, 16)   2320        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 32, 32, 16)   0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 32, 32, 32)   4640        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 32, 32, 32)   9248        conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 16, 16, 32)   0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 16, 16, 64)   18496       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 16, 64)   36928       conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 8, 8, 64)     0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 8, 8, 128)    73856       max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 8, 8, 128)    147584      conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 16, 16, 64)   32832       conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 16, 16, 128)  0           conv2d_transpose_1[0][0]         \n",
      "                                                                 conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 16, 16, 64)   73792       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 32, 32, 32)   8224        conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 32, 32, 64)   0           conv2d_transpose_2[0][0]         \n",
      "                                                                 conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 32, 32, 32)   18464       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 64, 64, 16)   2064        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 64, 64, 32)   0           conv2d_transpose_3[0][0]         \n",
      "                                                                 conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 64, 16)   4624        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 64, 64, 16)   2320        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTrans (None, 128, 128, 8)  520         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 128, 128, 16) 0           conv2d_transpose_4[0][0]         \n",
      "                                                                 conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 128, 128, 8)  1160        concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 128, 128, 8)  584         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 128, 128, 1)  9           conv2d_18[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 485,817\n",
      "Trainable params: 485,817\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "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(8, (3, 3), activation='relu', padding='same') (s)\n",
    "c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (c1)\n",
    "p1 = MaxPooling2D((2, 2)) (c1)\n",
    "\n",
    "c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (p1)\n",
    "c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (c2)\n",
    "p2 = MaxPooling2D((2, 2)) (c2)\n",
    "\n",
    "c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (p2)\n",
    "c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (c3)\n",
    "p3 = MaxPooling2D((2, 2)) (c3)\n",
    "\n",
    "c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (p3)\n",
    "c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (c4)\n",
    "p4 = MaxPooling2D(pool_size=(2, 2)) (c4)\n",
    "\n",
    "c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (p4)\n",
    "c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (c5)\n",
    "\n",
    "u6 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c5)\n",
    "u6 = concatenate([u6, c4])\n",
    "c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (u6)\n",
    "c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (c6)\n",
    "\n",
    "u7 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c6)\n",
    "u7 = concatenate([u7, c3])\n",
    "c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (u7)\n",
    "c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (c7)\n",
    "\n",
    "u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c7)\n",
    "u8 = concatenate([u8, c2])\n",
    "c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (u8)\n",
    "c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (c8)\n",
    "\n",
    "u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)\n",
    "u9 = concatenate([u9, c1], axis=3)\n",
    "c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (u9)\n",
    "c9 = Conv2D(8, (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": "markdown",
   "metadata": {
    "_cell_guid": "72330944-6ce7-4070-b276-c3c4b20c4fe5",
    "_uuid": "92350b6e18cc50f3fa7b6e9a02d39fcbff8238f7"
   },
   "source": [
    "Next we fit the model on the training data, using a validation split of 0.1. We use a small batch size because we have so little data. I recommend using checkpointing and early stopping when training your model. I won't do it here to make things a bit more reproducible (although it's very likely that your results will be different anyway). I'll just train for 10 epochs, which takes around 10 minutes in the Kaggle kernel with the current parameters. \n",
    "\n",
    "*Update: Added early stopping and checkpointing and increased to 30 epochs.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "9415b1c4-aa69-41b9-a1e3-d6053dbd4f64",
    "_uuid": "c060db22daa2abf12b28240cd81bbcbf1ce1bf87",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.4475 - mean_iou: 0.4181\n",
      "Epoch 00001: val_loss improved from inf to 0.29506, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 53s 88ms/step - loss: 0.4465 - mean_iou: 0.4181 - val_loss: 0.2951 - val_mean_iou: 0.4232\n",
      "Epoch 2/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.2412 - mean_iou: 0.4257\n",
      "Epoch 00002: val_loss improved from 0.29506 to 0.17167, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 51s 84ms/step - loss: 0.2414 - mean_iou: 0.4258 - val_loss: 0.1717 - val_mean_iou: 0.4368\n",
      "Epoch 3/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1663 - mean_iou: 0.4589\n",
      "Epoch 00003: val_loss improved from 0.17167 to 0.14400, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 50s 83ms/step - loss: 0.1663 - mean_iou: 0.4590 - val_loss: 0.1440 - val_mean_iou: 0.4884\n",
      "Epoch 4/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1290 - mean_iou: 0.5220\n",
      "Epoch 00004: val_loss improved from 0.14400 to 0.11136, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 50s 84ms/step - loss: 0.1285 - mean_iou: 0.5221 - val_loss: 0.1114 - val_mean_iou: 0.5523\n",
      "Epoch 5/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1155 - mean_iou: 0.5768\n",
      "Epoch 00005: val_loss improved from 0.11136 to 0.10928, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 51s 84ms/step - loss: 0.1155 - mean_iou: 0.5769 - val_loss: 0.1093 - val_mean_iou: 0.5990\n",
      "Epoch 6/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1007 - mean_iou: 0.6174\n",
      "Epoch 00006: val_loss improved from 0.10928 to 0.09795, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 50s 84ms/step - loss: 0.1006 - mean_iou: 0.6175 - val_loss: 0.0979 - val_mean_iou: 0.6340\n",
      "Epoch 7/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0998 - mean_iou: 0.6468\n",
      "Epoch 00007: val_loss improved from 0.09795 to 0.08905, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 50s 83ms/step - loss: 0.0998 - mean_iou: 0.6469 - val_loss: 0.0890 - val_mean_iou: 0.6590\n",
      "Epoch 8/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0921 - mean_iou: 0.6703\n",
      "Epoch 00008: val_loss did not improve\n",
      "603/603 [==============================] - 50s 83ms/step - loss: 0.0923 - mean_iou: 0.6703 - val_loss: 0.0918 - val_mean_iou: 0.6799\n",
      "Epoch 9/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0913 - mean_iou: 0.6889\n",
      "Epoch 00009: val_loss improved from 0.08905 to 0.08385, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 50s 84ms/step - loss: 0.0912 - mean_iou: 0.6889 - val_loss: 0.0839 - val_mean_iou: 0.6968\n",
      "Epoch 10/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0925 - mean_iou: 0.7038\n",
      "Epoch 00010: val_loss did not improve\n",
      "603/603 [==============================] - 50s 83ms/step - loss: 0.0922 - mean_iou: 0.7038 - val_loss: 0.0901 - val_mean_iou: 0.7101\n",
      "Epoch 11/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0861 - mean_iou: 0.7161\n",
      "Epoch 00011: val_loss did not improve\n",
      "603/603 [==============================] - 50s 83ms/step - loss: 0.0862 - mean_iou: 0.7162 - val_loss: 0.0927 - val_mean_iou: 0.7215\n",
      "Epoch 12/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0829 - mean_iou: 0.7268\n",
      "Epoch 00012: val_loss improved from 0.08385 to 0.08038, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 51s 84ms/step - loss: 0.0830 - mean_iou: 0.7268 - val_loss: 0.0804 - val_mean_iou: 0.7314\n",
      "Epoch 13/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0809 - mean_iou: 0.7357\n",
      "Epoch 00013: val_loss improved from 0.08038 to 0.07635, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 50s 83ms/step - loss: 0.0813 - mean_iou: 0.7357 - val_loss: 0.0763 - val_mean_iou: 0.7401\n",
      "Epoch 14/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0866 - mean_iou: 0.7439\n",
      "Epoch 00014: val_loss did not improve\n",
      "603/603 [==============================] - 56s 92ms/step - loss: 0.0864 - mean_iou: 0.7439 - val_loss: 0.0807 - val_mean_iou: 0.7473\n",
      "Epoch 15/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0818 - mean_iou: 0.7503\n",
      "Epoch 00015: val_loss did not improve\n",
      "603/603 [==============================] - 57s 95ms/step - loss: 0.0816 - mean_iou: 0.7504 - val_loss: 0.0771 - val_mean_iou: 0.7537\n",
      "Epoch 16/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0813 - mean_iou: 0.7569\n",
      "Epoch 00016: val_loss did not improve\n",
      "603/603 [==============================] - 57s 95ms/step - loss: 0.0812 - mean_iou: 0.7569 - val_loss: 0.0798 - val_mean_iou: 0.7596\n",
      "Epoch 17/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0782 - mean_iou: 0.7621\n",
      "Epoch 00017: val_loss improved from 0.07635 to 0.07304, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 57s 94ms/step - loss: 0.0783 - mean_iou: 0.7621 - val_loss: 0.0730 - val_mean_iou: 0.7649\n",
      "Epoch 18/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0843 - mean_iou: 0.7672\n",
      "Epoch 00018: val_loss did not improve\n",
      "603/603 [==============================] - 54s 89ms/step - loss: 0.0842 - mean_iou: 0.7672 - val_loss: 0.0775 - val_mean_iou: 0.7694\n",
      "Epoch 19/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0776 - mean_iou: 0.7716\n",
      "Epoch 00019: val_loss did not improve\n",
      "603/603 [==============================] - 53s 88ms/step - loss: 0.0778 - mean_iou: 0.7716 - val_loss: 0.0732 - val_mean_iou: 0.7737\n",
      "Epoch 20/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0791 - mean_iou: 0.7757\n",
      "Epoch 00020: val_loss did not improve\n",
      "603/603 [==============================] - 53s 87ms/step - loss: 0.0790 - mean_iou: 0.7757 - val_loss: 0.0766 - val_mean_iou: 0.7776\n",
      "Epoch 21/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0783 - mean_iou: 0.7792\n",
      "Epoch 00021: val_loss did not improve\n",
      "603/603 [==============================] - 54s 89ms/step - loss: 0.0781 - mean_iou: 0.7792 - val_loss: 0.0840 - val_mean_iou: 0.7811\n",
      "Epoch 22/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0753 - mean_iou: 0.7829\n",
      "Epoch 00022: val_loss improved from 0.07304 to 0.07093, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 55s 91ms/step - loss: 0.0755 - mean_iou: 0.7829 - val_loss: 0.0709 - val_mean_iou: 0.7846\n",
      "Epoch 23/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0745 - mean_iou: 0.7861\n",
      "Epoch 00023: val_loss did not improve\n",
      "603/603 [==============================] - 52s 86ms/step - loss: 0.0744 - mean_iou: 0.7861 - val_loss: 0.0725 - val_mean_iou: 0.7877\n",
      "Epoch 24/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0756 - mean_iou: 0.7891\n",
      "Epoch 00024: val_loss improved from 0.07093 to 0.06879, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 52s 86ms/step - loss: 0.0756 - mean_iou: 0.7891 - val_loss: 0.0688 - val_mean_iou: 0.7906\n",
      "Epoch 25/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0755 - mean_iou: 0.7920\n",
      "Epoch 00025: val_loss did not improve\n",
      "603/603 [==============================] - 52s 86ms/step - loss: 0.0756 - mean_iou: 0.7920 - val_loss: 0.0857 - val_mean_iou: 0.7933\n",
      "Epoch 26/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0803 - mean_iou: 0.7944\n",
      "Epoch 00026: val_loss did not improve\n",
      "603/603 [==============================] - 51s 85ms/step - loss: 0.0803 - mean_iou: 0.7944 - val_loss: 0.0723 - val_mean_iou: 0.7955\n",
      "Epoch 27/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0743 - mean_iou: 0.7967\n",
      "Epoch 00027: val_loss did not improve\n",
      "603/603 [==============================] - 51s 85ms/step - loss: 0.0744 - mean_iou: 0.7967 - val_loss: 0.0757 - val_mean_iou: 0.7979\n",
      "Epoch 28/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0725 - mean_iou: 0.7990\n",
      "Epoch 00028: val_loss did not improve\n",
      "603/603 [==============================] - 53s 87ms/step - loss: 0.0723 - mean_iou: 0.7991 - val_loss: 0.0710 - val_mean_iou: 0.8002\n",
      "Epoch 29/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0714 - mean_iou: 0.8013\n",
      "Epoch 00029: val_loss did not improve\n",
      "603/603 [==============================] - 53s 88ms/step - loss: 0.0712 - mean_iou: 0.8013 - val_loss: 0.0703 - val_mean_iou: 0.8023\n",
      "Epoch 00029: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "earlystopper = EarlyStopping(patience=5, verbose=1)\n",
    "checkpointer = ModelCheckpoint('model-dsbowl2018-1.h5', verbose=1, save_best_only=True)\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=30, \n",
    "                    callbacks=[earlystopper, checkpointer])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1f381f5b-1b71-4daa-a417-e02f4894540b",
    "_uuid": "bb15226ea617cf91ed8f43179fccb5a15809e5a0"
   },
   "source": [
    "All right, looks good! Loss seems to be a bit erratic, though. I'll leave it to you to improve the model architecture and parameters! \n",
    "\n",
    "# Make predictions\n",
    "\n",
    "Let's make predictions both on the test set, the val set and the train set (as a sanity check). Remember to load the best saved model if you've used early stopping and checkpointing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "2daa48d5-ac98-4e18-af3f-a582baaa44f0",
    "_uuid": "f841760b4abca1a25cb750822f88268bd79bf2ce",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 16s 26ms/step\n",
      "67/67 [==============================] - 2s 25ms/step\n",
      "65/65 [==============================] - 2s 26ms/step\n"
     ]
    }
   ],
   "source": [
    "# Predict on train, val and test\n",
    "model = load_model('model-dsbowl2018-1.h5', custom_objects={'mean_iou': mean_iou})\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": 9,
   "metadata": {
    "_cell_guid": "649248cd-a1fb-4da6-ade2-4bebad44bcab",
    "_uuid": "7e06242a50870e07a080064a4912b761775990fa",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JrPPOqlhMrn6n26SO0eVGG7X9fNZbtmwBMGLBXD307NmzxTFGznRUjL/zndDb\nn8c2fPC5Yw6H46rHxDChMsaRm5DbkzeZ35XSmSz3lWJA3EWEWxtp78jtiJ988sni/4RmRLkaRZ2t\nbTWFLnKT2rCYXHaSimKW2zA3i1yDLIUsd8eOHQCA8+fPAxi8G5rRUEci46F2uG3bNgCj7dBjq0+s\n5Dw9Z0IOh6NXTAwTKnujLnN6mp7XxTy2ujqsOUj8pCd72cteBgDYuXPnkvNmZ2cBjPSDVatW4Ykn\nngAwYkeaAeVmmXfBTK1tVcUkUmia11WFlH06Pyt2fhXLsuZl8ZMMiNEwMqBy9JNRMJYlc6ZGxO98\nT/jJOmMZ1SuBiemELOJg3RCpa9pYl2TWhUCdO3TgS7N7924Ao3AthUb9fXp6unih9u/fD2D5cCwm\nrubeV04Z6zKkuh4LYvbErllVPjbs0nVpwVeXK3f4sfbWiHVSHFJx6WKG2csdITsbDtfZ6RAMYPD9\n4HCNyYxddj65f4s+HHM4HL2iFRMSkc0A/gjAbQACgJ8D8A0AnwBwA4CnAfx4COFETr05w5u2AlqT\noUddYloV2thIb8cENT2JcePGjQBGno8ebXp6GjfeeCMA4MiRIwBGXlAnMaa8fxPEnl1d2kL5s+5Z\n6++pZMSchMgYc9PJoWxvzTjYtmXWmcv69CfrpJhMW8huNmzYUAzRGLbnffCZE3yPuEVWFwGB2H1Y\n0ZYJfRjAX4YQXgngNQD2A7gLwP0hhJsB3D/87nA4HJVozIREZBOA7wPwHgAIIVwCcElE3g7gDcNi\n9wL4GwDvT9XXVoNJebuUaGlBKmU/xzZrSJufZD70sPSGFCTpJTnJ8dKlS4UHZTj/zJkzxW/A0ike\nFlvqbE5pKPq7bjMtmqfSCMp1WZ9hip3VlWH7U/gno6BWx3LUbcg0+B2Ib7wZs1MzIT43TsEgVq9e\nXdjB94NaD+2mYK3F7hj7sqArHbYNE7oRwLMA/kRE/lFE/khE1gPYGUI4CgDDzx1VJ4vInSLyoIg8\n2AX9dzgcVybaaELTAF4L4BdDCA+IyIeRMfQKIdwD4B4AmJqaClWJb6kIRLlMCrFyTSI8ueXrkuOs\n0JoEPR4jJfR8ZEKrV68uNAN6Y7ImlmUdKY0rJ3Uipv3EFuQi49HbdBNVUZuU17Ym4JXfL83YdBky\noJtuumnJcW0L25jRp2effbZ4RnqqRCwiFbsvtg31HzLbVatWFdejnaybn7w/MjRti77vlC1VZZoy\nozZM6BCAQyGEB4bfP4VBp3RcRHYNjdoFYK7FNRwOx1WOxkwohHBMRJ4RkVtCCN8A8CYAjw7/3QHg\nQ8PPz+TWHdMLVoK1lI+n6modknWDAAAgAElEQVSTJ2S1j5/UA7Qmwe+sm3kiIlJ4TB7btWsXgIF3\nttgTS7KznKNZLO287rrrAIyY0cmTJwGMNAraXF6oK7YAlzXZMmXbzMxMoanwmM6pYn4Wo086Ksbn\no5NGZ2dn8eSTTy65J2vekAbL0Ta23eXLl4u6aQe/006yYT77mD7VZWKqFW2TFX8RwEdFZAbAkwB+\nFgN29UkReS+AgwB+rOU1HA7HVYxWnVAI4Z8AvK7ipzc1qKv4f042dBfZy9ZrWcuk8ody8p14nLoC\nx/TMD6IGRM9XZlD0xvSc27dvBzBiIbFIVZNAQaxNeC1G6GgDy1ObYC4TP8mMLl26lNR2Ukw5FfHa\nsWNHwXAIMgrawfbWUTLNNjW7Wbt2bTHJmPeqda9U+8eyscu6IO2YmZlZYqde7pXvSSpSZ4no5uTz\n1cEzph0OR6+YqLljRBMNIgbrpMaq47ksK2ZblQexegt6PXq6559/HsBoSYYDBw4AGDGisjbEiavU\nDug5tZ7RBrH7KOstwIgBceKtjt7Qc/P7wYMHC5tjzIHtGptMGmOgvH9GlNasWVPYqe2nXXohMdpE\nBqS32GFbr1mzprh3rcXFImypaCVRNT8tFvFM5V91kUdHOBNyOBxXFCaGCZXZgYV5pHrsWF1WZlSu\nLzdD2oJclkUPR4bAWfNkRPydnnnr1q1F3dSRqCs1yY5NQbOSWKYxGZ1evJ3shDlMZA2XL1+OevEm\n0buyjeWlcTWbot1kSLw2NRUdlaT9OgI2NTVV3CsjZ9RnUqwkhrryuWzKGnGsgm5vtmEu03Ym5HA4\nesXEMKGciFFV2a4zp6s0IWvUyzI3KKYDxOqm1kA2w9wTHqfHJpO4ePFi4ZFYFzNsyZr0tXLzbsrn\nxHQZzYzIIBg1I3MgS6C2xXyiF198cZm9KftS98F2ob6zevXqZevw8Jp6FjrBcrH1hcqftEdni8fs\ny51bJrJ8q2mrzqTLx1AefaRGHXy2VjgTcjgcvWJimFDufKq2GdJNvH4M1jrr5o6lGBE9HT0zZ81r\nBkHvXs44Zq6LXia0DZPTiNmv5y7t2bMHwCjKx/PoPfmdUbTDhw8vm/Wvrxn7norckZmsX78+GYHT\nXp42aeajl1WtYoiaPel8pxQs0dau9L5yO6TaW6/sMDdnm7HlTMjhcPSKiWFCdXlCVV7WyiRi5WPn\n52RuW5lDnYe25mvorGDOBTp06BCAkRci29m9e/eyeVlPP/00gOURnJgtMZuqyqUYpF5onas+MnJH\nWxmxoiYzOztb2B9jDFYGFJs7trCwUDBIHtN5TJr56GuzTRkJI9ObmZkpzk3ZmdIeLdpcrK6mzKju\nWcfsZ3a5Fc6EHA5Hr5gYJtR07liqTMqL13lNq/ewXrvqt1QdGnp7X+bdkOWQUZw4caLQL7j1DyNr\net5QEw3ICp1fQwbHKB7nVZ04MViGnDaTmWzatKlgS2QUuc9SoyrjWrMsMje9HjcjjNSsYqtSkvGt\nXr16WV4Q69bPMGVvDrrMAaMNsfdFRx2ph1nhTMjhcPSKiWFCltwZIjeSpq9Td7yOHeSO2S0Ri9yI\nlPbY2l6uP0xmASxftdAaCcrJHSkfK39qJvHCCy8AGM0lO3z4MIARG6D+Q2zZsqUok8qQzo2Yllml\nZilkMmRmZDO0n5neZHQ694ja1tmzZ4sy5Yhl2c4m2cox5LZBTr3WXKPcjOmJ6YTqhmMaOQ2sl4a1\nPqSqZMXU9XPF8apzLB1w3fe6ZS1SCW2xOmOoSxrVnZBeiF9vusfhDZMZOXTkH33V9VIJeTnlY8uh\nMgmUk4HZuXMYSRFWL5vC5Mfp6ellKQZ6adXc5ERLWX3PXXZKqXeTz9YKH445HI5eMTFMqGv6SKS8\nTZ13SXmVpinwTZIy9bnalqpEN0saQleI2UlmwMS1V7/61QBG4iUXO+NwjExDL7Nah9zhjBbmFxYW\niuVyKTxzWEgmREGddvJ+NHPjfbHuM2fOFPekGZH1eYzjeTVB6l3kfbEtrHAm5HA4esXEMCELyl4/\nxSY0U0gJz1WswZpMlhJ2u0ip7yINoGlSpSUUHmtfekeyAeohZBxPPfUUgFGqP1kKw/KnT59eJm5r\ne2PJczGwHrKbTZs2FeeS2ZC10KvrKS9kQrSbQjY/y8uq8t6bTjtpw4RytcYmLF3rrq4JORyOKwpX\nFBPK6a1zowQ5UytyWVjd8ZQuYPVMXegGbXS5mFfXy9MySsZQNq/5zDPPABgxJWJubm5ZSoG1fWPf\n9bbK586dK+xhVIt2sIzeMJL3xSVV9u3bB2A5K7h06VJx7zoCl7K7Sy2oy4izhn7WOhUhBWdCDoej\nV0wME8rNE8r1Ek2ndViQO54u601Nx+hNpyzU1dGmrtQ1yCweeeQRAKMoEvNv9LbW5eTF2DKo+hpW\nb8/6qPOsWbOmmO5CnYj20G7NgHQdzGcio6KmdfTo0eSEYWu01TKBdSXyhFI2xKayxOBMyOFw9IqJ\nYUKWbGJ9vKpMW4ZQrifGVnI1iaq6Ndp6+arzUvZZtS0ixxNrnYBMggvY79+/HwBw/fXXAxgxIzKL\nxx9/HMCAmTRdckRDZ0OTdW3cuLFgK/yNjIgalmZCWrc5evTokvPKUb7YuRqx56V1ppxzV4IBaVgX\nZyOcCTkcjl4hk5CNOTU1FbgYFJDO8ekCOXlCVq+Sw8pS3rBp1nUX+kEKOXXrT05uZCYyNRROFCWT\nYFTq0qVL2VvjWL1/ebIq57SRkVEjojYVy1XitWLb3SwsLJjt7+L9tuaq5dZjgW6LU6dOPRRCqNom\nfgmcCTkcjl4xMZpQGSkPXTXDvW72uOUa+lrl33PzUfR5Fo+XYkBdRO9idcUYpzUyl1NWzySPbT9U\nZg/W/J86+6psLOfycAkU/T6lWEzs/sq2xcqOM0M69l1fO/a7he1at99OoRUTEpFfFpFHRORhEfmY\niKwRkX0i8oCIPCEinxCRmXRNDofjpYrGmpCIXA/gywBuDSGcF5FPAvg8gLcC+HQI4eMi8ocAvhZC\nuLuuLq0J1Vxz2bFxaVqWvKWmuTxNbG6rFXVtj+V6lvNiLKwua7jtfWiby8u7algjc3XXiDGgcWid\nXaOJjTznzJkzK6IJTQNYKyLTANYBOArgjQA+Nfz9XgDvaHkNh8NxFaOxJhRCOCwivwXgIIDzAP4K\nwEMAToYQONA/BOB6S325WdC5Eavc+iz25M7HqbLZyqaaeP/UOblevcqDN41QxbSUurIp1mT12lV6\notXeXPab83xysRK5QCvB0hozIRHZAuDtAPYB2A1gPYC3VBStvAsRuVNEHhSRByeZjjocjvGiTXTs\nBwA8FUJ4FgBE5NMAvgfAZhGZHrKhPQCOVJ0cQrgHwD3AQBPKyQat6vmtGdNtc0vKdeR6oBydIHZu\nzv2lGEPMlhzG1JR9xK5tuUZO1K7OxnJ9bXOkVoKVWGxpa0fd+bnP2oo2mtBBAK8XkXUysO5NAB4F\n8CUAPzoscweAz7S4hsPhuMrRRhN6QEQ+BeCrAOYB/CMGzOb/Avi4iPzX4bGPWOvUPW3dnBn+lhvV\nSOUVlcul8mliyMlatXjpqvKxa6aOVR1PMZ8c9pLbNhbb2ubXWBiSleHksoBcrbMJytdoy8Ryoqtd\noVWyYgjhgwA+qA4/CeA729TrcDheOpiojGkrg1i1alXBhGJZm9Z1iS2wRsFyNIkUw6mLSFlsq/PA\nuVGyunqsz8zKNJqwrlwtyJJvlopsto2c1p2TyzhydLWcc5oi994nqhMi6jofYDBJkBMg+cmJg1yS\ngRMg9YJSqQWXLBQ9Zq+uI1Z31TlV10/VUVeu6ri1Y7PCMoyJ2dAFmnaelvvtaniT07bj7BiapHh0\nda0UfAKrw+HoFRPFhGIUnp9c7mHNmjXFXuY33HADgBHT4eJSzz//PIDR1jKxhaWs4d+ukRvqtdpb\nF6JPXauNIJ06ZyXauY13twrmTdlxW/us17Ow8KY2jOvZORNyOBy9YmKYUF3vSi2IC05df/31uO22\n2wAs36zu2muvBQA8/fTTAEab65ERURNKebw6EbkLWAXcVLk2msM4PXMMuRpS+Ryrjtbkvqx2tbnG\nJIjBbWwY1yjBmZDD4egVE8OEytBaEBkQl+C87bbbiqjYhg0bAIx6eC6W9bKXvQwAisWquHmdToBs\no1nkJhqWf2/LeCyJk+MKO5fL5WoROXXra1jrbuLtu4oc1qVjtIlC1tmYU0esziZ2dAVnQg6Ho1dM\nDBOqYgdaC3rFK14BAFi/fn2xQLreKoZsib02o2iMllE7aoOU19RsK/Z7+VjsuzXpT58vsnzaSSox\nMFZXHbqaJmBJiNSwMoscJpJinm2iSk2ZRO470KTOFHLYey6cCTkcjl4xMUyo3Jvy/8yC3rRpEwDg\nuuuuAzCIgG3ZsgXAaIsYZkZv3LgRwGgbXpYjY9LTPHKiMrkaUI4HazrtQdtQ/q4ZTy5zSKFJxnTq\n9xh7awOL1pWbwd6EKXXNGscR3cx5/7rK/XIm5HA4esXEMKEyNBNiVjRzgLZs2VL8xrLcpI7bDDO7\nmp8p3aYKTec/pSJFVZGT3C2LUnWX62nKSlKoWyA+xWi6jGw1RQ4zbVJH27o1UmysC7SJEje1y5mQ\nw+HoFRPFhLSGQRZDnYc97NmzZwvmQ71odnYWALB27VoAI2aUyruxjPlzc130p16ALYTRAvGxDO7c\nqJk+Xjd3SdfZ5v70kiqsU8/R099zM8ZzYGUalmecel+aaEMryfaaoo1+lXuuMyGHw9ErJooJaehI\nFrOhp6ensWfPHgCjvB8yoGuuuQZA3NPmjKtTzCHmxWk3mRyzu8v6j85X0usdxa6Vq7FUnZvSY1K/\nl9kPo446+qjXdWKdOes58Xtb7SRWt+Vca06VBeOIZrW91riyoHPgTMjhcPSKiWJCMb2ATIIz4bdv\n315kSL/wwgsARkyIc8V4Dj1vygPXeYQUq9LekqyAM/yZtU2bTp8+jZMnTwJYnueUyu2J2WBhRlZ9\nLHZ/5ZUtgcG6Tlu3bgWwXLfj/fGTLNZqv/7dgpT3t2Rl50bFLM8pN+epi3ygttdaSTgTcjgcvWKi\nmBChc2foecka5ubmitnzLEPWQbZEZnHs2DEA6ZUVq2zIzREpMwQAePnLXw5gNJ+N9s/Ozhasgkwu\nFk2KwaJ5WSM3sTp0VI/fyej27NlTMFAyIbYzo5X8Hmv/2H1Z7LPCwizaZLvH6oydm3p2fUTP2jAg\nz5h2OBxXNCaSCWk2wCxoaisLCwtFGUZfDh06BGDEiE6cOAFgwJrK5XSvrTOVm+QH6WjY3r17l9hL\n1saZ/NPT08U5WhPS2pWVvVQdbxohiWWXU+ti5vrMzEzBeLZt2wZg9Kz0GlDU8MiI6uzWNluZgTWX\nJ3bNqt9ijCjVtnWMrw2rGjeasJq2OtLEdEIhhGWNzxf27NmzAIBdu3YBGPyhcpEydjL8jQLokSNH\nAIxe/tRwp+qBWx+IFtCZPkDRVg/T5ufni2EMy/B+2BnFkhnrOs2YXbF7TNWh/+BoPycFT01NFZ0N\nh8F0AuycWPfx48cr6+wiGbFp+kLV840NQWOJqPq5VHU+sbSQVKJsH52RC9MOh+Mlh4lhQiLLF+Ei\nC6B4S1YzMzNThH5J93kuhzdc4F4Pw8rXqzreZroGmQKHZWQ1ZD1kB6tXry4YGwVdnkv7U3bGfi9/\nj3lUq+jK42RyHHKRHVxzzTUFEyJ4P2RGOqyfalMLC8hNRozddzkZNpaGwE8+U31cv6s6CbUsHcSQ\neh59TvNYieGZMyGHw9ErJoYJAXHRmKyHGxsuLCwUrIKMgmUpSJM96dBwKvRtSVaMeS6yGU6epUak\n2c309PSyZEqyJepfZFEW+2LlrCHf1P3pZVGo+1TVcfr0aQCje86d9mCZtmFhsVXX1EJ7uRyZDZkd\nnw+fKacDrV+/vvK++B5S++JzLDNSS1qF5X4mXSPKtc+ZkMPh6BVJJiQifwzgbQDmQgi3DY9tBfAJ\nADcAeBrAj4cQTsig2/wwgLcCeBHAe0IIX801SkewyCy4oeHFixeLCA29Mss8/vjjAEaemEwoN+JQ\njtalNBN6VtqiJ3MyRE+baGv5Ny5JQibHcy1RvBisjEFDt5XWRcpMlcd4T2wDnULBz1SyouU+rM8y\ndbys/5D5kJEyzYJleFwvI8xnSobEtjp48CCAAdNN3bM1pcDSZisx/cKqNVphYUJ/CuDN6thdAO4P\nIdwM4P7hdwB4C4Cbh//uBHB3I6scDsdLBkkmFEL4WxG5QR1+O4A3DP9/L4C/AfD+4fE/C4Ou8e9F\nZLOI7AohHDVcJzpW1vlC3/rWt4r8IGpD9LQ638Y6TaCuV7d6XrICekXqCYwU0ZuWExLJmuhZ9SJh\nVtRpWZaydeX0/ZeXG+Fv5chfGUzQtCZhVtmYywys0cByEiafGTfNJKMheH88h9/5npVzwABg586d\nAAbvaixBcxyaT4q1WyOHOddoy7qaakI72bEMP3cMj18P4JlSuUPDY8sgIneKyIMi8mCfIUiHw9Ev\nuo6OVXWJlT1MCOEeAPcAwNTUVKiKgmiUI1166QudrxFjQKkISh0Dip2jx/Lliaplu/k5NTUVzTvh\ncX2tVGSlzqPlZhTH2pRsk/laW7duLZinvndqW2RCsQms49AwrAyozFy/7du+DcBI09L5QtTu+N5R\nw9P6kl6SeGpqKqotrmSmdFtWXDVS6QpNmdBxEdkFAMPPueHxQwD2lsrtAXCkuXkOh+NqR1Mm9FkA\ndwD40PDzM6XjvyAiHwfwXQBOWfQgoDpTOeYpylpErJ4UA8pR+K1lqf2QMWj9oLzMBfNKyByopcQy\ni61spk7T0mVT90fQbkbAmDMzPz9fMBveI5kRz2GOV2xRuZh3rXq+bRmCZiS0effu3YWmw/shoyED\nYt4Z5yjqOvk89WTfKm3PymrboCnDjL0r44y2WUL0H8NAhL5WRA4B+CAGnc8nReS9AA4C+LFh8c9j\nEJ4/gEGI/mfHYLPD4biKYImOvTvy05sqygYAP9/UGKuCH0KI5s80zV2o87Kx3/Rx6gU6MkcGUWYD\nOrqS8n7WzRHLv8faItfT0m7qPNRNzp07tyxLnIyAi8npjHbt7VPPyfL8UoxCz4Qny+EKBuvXry+Y\nG3/jPWv7qYfxdzJZnZFfFRHLzdtqM4esKXOpO29cOUieMe1wOHrFxMwdq5sjpFHnEXKzTi25GikP\noBkPwXlUZD3MyqV3BUZMiB5VZyXX2WVFihGlvvP+aCNZzpYtW4p7oednGc0YcrWsJsw0Vo5geeo1\n1LbWrVtX3KN+RuVtpoDRM2Z5/s466/LSrM/OmudU/n0l0lxSWmLTd9SZkMPh6BUTw4Qs7Kbc4+Zq\nQLlZt5ay+to6j4bekx63HDXTGgTZ0jhyRGL25n4nq2EE7NKlS0U0T+cB6Tl7sdULUnpVlZe3nFMF\n2srMdc4/XFhYWLZRJZlNTPvhNVhOvwNkuPPz840ZUAxWTclSRxN9JxXdy4UzIYfD0SsmhglVoUmP\n3zQqloOYZqKjSFz1kbklBw4cALB0PR6dTa0jaylY7jcnS7yuLtZDWxcWFpaVbTJLvs5GS/5YLIdK\naxX8ZLYzo2bz8/NL/g+MtB69VjZBVkXmU86dAoBTp04BSG+6WbYrdtzSlk1ZbhO01YA0nAk5HI5e\nMdFMyILc8TNh6cWteUsEvR6zhOkVubZMOZtWbwPEqIvWHnIyo7WNKW9nzU/R5Ztkl3cBq1dPXZss\nhtpW+Rifg4526V1PdFY8QSbLyOji4mI0N0rba7W/Crm6WRfoqk5nQg6Ho1dc8UyISM2t0sjRl6zl\n6C3pTbn+kdYgQgiFZkBPy7J6dxDr/WjURRDLZerqbhpxtNTZBk2je/xk9LK8BhKZELUgzYwYNeNz\n4rPUGe96XasyEyLa5jnlwNo2OeiaXU1MJ2QZPqTOt2Al6ClfTE565IJZ3DLnxIkTBV1nx8Xv1mkc\nTezKRc7zSIVtrdMyqjrdnKkqdbaxw6cDKC+3wXZnx6Q7IT4ndkY6FYHl+Bz1crZ1dreRDFgu1bmM\n831q21n6cMzhcPSKiWFC41wqoIwcxmQd0sWS+siEOFGSXhQYeUoK0eUtYqo+rSjblBJwrUMmixie\nmn5htbsLe1LDR7Z9eYNGBgo4HNPsiZ9kSOVkRGDEhKqWs80ddunzcobTK8mIYtfMhTMhh8PRKyaG\nCU0a2oiA9JoUJ5977jkAIyZ0+fLlwoOSLeklI2JoomlZk+Fyx/ZVv+fa10RLsnreFFMtL01CVsTn\nwqkdFKDJWHkumSu/c+OF2NIeVXZY76cpk7KUtepSVXZ2xbKcCTkcjl5x1TGhcUa/YtfQHoLej7oB\ntwYmZmZmCo/J32ILwceu2QZtdKaq75a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2facf8e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d981b3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d9816a518>"
      ]
     },
     "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": "markdown",
   "metadata": {
    "_cell_guid": "af602aea-5e56-42a8-9331-54b4b2650593",
    "_uuid": "5fcee2b9aee2fba5c60d43ad48a14139e9c1318c"
   },
   "source": [
    "The model is at least able to fit to the training data! Certainly a lot of room for improvement even here, but a decent start. How about the validation data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "4f66b75c-c694-41a1-8c91-34bb6595837b",
    "_uuid": "d4ccbb559375bc2777ffb692a20adc313159f2cc",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X9dN+5y3RKMlvZlk9aK97jR+2xldbwjOmHccZlFHeO6aJPGijAJJHP96f2y7Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d98085198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d980eee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ErGy9HfhMRIwCJ4Ab5vsZZjZE+nxjdhl6vYa4CPhnkk4C5wHHgHfzWlv+HuC3gTt7/JwF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d94fe5320>"
      ]
     },
     "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": "markdown",
   "metadata": {
    "_cell_guid": "a6690535-b2e4-49ac-98d9-7191bfabfb6f",
    "_uuid": "6a34c98de7c6ae473f676a34fe7e099b46764eca"
   },
   "source": [
    "Not too shabby! Definitely needs some more training and tweaking.\n",
    "\n",
    "# Encode and submit our results\n",
    "\n",
    "Now it's time to submit our results. I've stolen [this](https://www.kaggle.com/rakhlin/fast-run-length-encoding-python) excellent implementation of run-length encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "59a0af60-a7d7-41ef-a6fe-9e3c72defa07",
    "_uuid": "4f99c1bf852e82b60bd4f982ca0df293f712cdf0",
    "collapsed": true
   },
   "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": "markdown",
   "metadata": {
    "_cell_guid": "31133f8c-3f40-4dff-8e1d-898d56672332",
    "_uuid": "2e07f6afc4787b068ba714428145dcb3951d718f"
   },
   "source": [
    "Let's iterate over the test IDs and generate run-length encodings for each seperate mask identified by skimage ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "22fe24a1-7659-4cc9-9d23-211f38e5b99f",
    "_uuid": "089587843ed6a3955fdcb9b23a6ec3bf5d703688",
    "collapsed": 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": "markdown",
   "metadata": {
    "_cell_guid": "20b6b627-0fd6-425d-888f-da7f39efb124",
    "_uuid": "849184a40a2c9c21506d8b8eb10ad9155fa229e8"
   },
   "source": [
    "... and then finally create our submission!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "1ba0ee3a-cca0-4349-83f6-09a1ac6fcb44",
    "_uuid": "ba589f56f5be1e6886bc88f5bf9e7d0a408e4048",
    "collapsed": true
   },
   "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-dsbowl2018-1.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "222475b9-3171-461a-90f0-a820a6bd2634",
    "_uuid": "fb5e6f8cca872f1bd7036f6d9ac2ed2cab615536",
    "collapsed": true
   },
   "source": [
    "This scored 0.233 on the LB for me. That was with version 2 of this notebook; be aware that the results from the neural network are extremely erratic and vary greatly from run to run (version 3 is significantly worse, for example). Version 7 scores 0.277!\n",
    "\n",
    "You should easily be able to stabilize and improve the results just by changing a few parameters, tweaking the architecture a little bit and training longer with early stopping.\n",
    "\n",
    "**Have fun!**\n",
    "\n",
    "LB score history:\n",
    "- Version 7: 0.277 LB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "3f5e5a47-6133-4870-976a-a8e4fa7bf46c",
    "_uuid": "2a83eab66bf55194f300953bea5534b6a043130f",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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