TGS_salt_identification_challenge12_Google_Colab.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://pbs.twimg.com/media/DilKBjUVsAA74j1.jpg) "
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Background Data\n",
    "\n",
    " *  Where there is salt, there is oil.\n",
    " *  But where is the salt?\n",
    " *  Classifying seismic imaging currently requires human (salt/not salt)\n",
    " *  Can we use an algorithm to do this instead? (yes)\n",
    " *  Seismic data is like an ultra-sound of the subsurface\n",
    " *  It uses wavelengths around 1m to 100m\n",
    " *  The Society of Exploration Geophysicists has 10K publications using the keyword 'salt'\n",
    " *  We can think of Earth as layered.\n",
    " *  Sand gets deposited on top of existing sand. And in comes the salt.\n",
    " *  There is an entire research branch dedicated to salt tectonics, that is the movement of salt in the subsurface.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our steps\n",
    "\n",
    "   * Install Dependencies\n",
    "   * Upload our Dataset\n",
    "   * View the labeled seismic imaging data\n",
    "   * Plot the depth distribution in the training data\n",
    "   * Plot the proportion of salt vs depth in the training data\n",
    "   * Build a U-Net algorithm to learn the mapping between seismic images and the salt filter mask\n"
   ]
  },
  {
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    "id": "9XD5SEFNn-H2",
    "outputId": "3b6bc3a4-4bc5-49d8-8f1f-d012c6cb1c0a"
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     "output_type": "stream",
     "text": [
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      "Collecting pillow\n",
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     ]
    }
   ],
   "source": [
    "#read/write image data\n",
    "!pip install imageio\n",
    "#deep learning library\n",
    "\n",
    "!pip3 install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl\n",
    "!pip3 install torchvision\n",
    " #access kaggle datasets from colab\n",
    "!pip install kaggle\n",
    "#model loading\n",
    "!pip install ipywidgets\n",
    "!pip install --no-cache-dir -I pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "jZc86wWdn-UW"
   },
   "outputs": [],
   "source": [
    "#file input ouput\n",
    "import os\n",
    "#matrix math\n",
    "import numpy as np\n",
    "#read/write image data\n",
    "import imageio\n",
    "#visualize data\n",
    "import matplotlib.pyplot as plt\n",
    "#data preprocessing\n",
    "import pandas as pd\n",
    "#deep learninig\n",
    "import torch\n",
    "#just in case we need a backup datasets\n",
    "from torch.utils import data\n",
    "#willl ouput the plot right below the cell that produces it\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": "OK"
      }
     }
    },
    "colab_type": "code",
    "id": "frlPd8gen-Xz",
    "outputId": "f8f89b9e-fc27-424e-a243-6376da35d0e8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-4e28b4bb-7618-44b7-9b3f-6593edb67200\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-4e28b4bb-7618-44b7-9b3f-6593edb67200\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving kaggle.json to kaggle (4).json\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'kaggle.json': b'{\"username\":\"ankit974gupta\",\"key\":\"a9e0357ae33fca7d1e70586041e92486\"}'}"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#allows us to upload files into coalb\n",
    "# we'll need to upload the kaggle.jason file\n",
    "#in kaggle, under accounts, click 'create new API token'\n",
    "#upload the kaggle.jason file that is automatically downloaded\n",
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dzNydtIcn-bD",
    "outputId": "6c898bd8-ea28-471f-ad31-2ab2e3ff785e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 root root 69 Sep 22 08:00 kaggle.json\n"
     ]
    }
   ],
   "source": [
    "#ensure its there\n",
    "!ls -lha kaggle.json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "IggS91Mfn-es"
   },
   "outputs": [],
   "source": [
    "#the Kaggle API client expect this file to be in ~/.kaggle,\n",
    "# so lets move it there\n",
    "!mkdir -p ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "\n",
    "#this permissions change avoids a warning on kaggle tool startup.\n",
    "!chmod 600  ~/.kaggle/kaggle.json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 124
    },
    "colab_type": "code",
    "id": "XlworwuKoVK_",
    "outputId": "5ec18cf8-932d-4c0b-946e-84b7b7639d3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depths.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
      "sample_submission.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
      "train.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
      "test.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
      "train.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "# lets now download our dataset\n",
    "!kaggle competitions download -c tgs-salt-identification-challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 124
    },
    "colab_type": "code",
    "id": "4B-KU56soZMB",
    "outputId": "86e27eec-fe6c-4803-b51b-4c6e1ac01dea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depths.csv\t kaggle (2).json  kaggle.json  sample_submission.csv  train.zip\n",
      "images\t\t kaggle (3).json  masks        test.zip\n",
      "kaggle (1).json  kaggle (4).json  sample_data  train.csv\n",
      "Archive:  train.zip\n",
      "replace images/8d08955cdf.png? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
     ]
    }
   ],
   "source": [
    "#and we'll need those training images unzipped\n",
    "!ls\n",
    "!unzip train.zip\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "NqkFO5D_ojZp"
   },
   "outputs": [],
   "source": [
    "#lets create a class to represent this data , to  make it easier to access\n",
    "\n",
    "class TGSSaltDataset(data.Dataset):\n",
    "  #initwith the location of the dataset, and the list of file \n",
    "  def __init__(self, root_path, file_list):\n",
    "    self.root_path = root_path\n",
    "    self.file_list = file_list\n",
    "    \n",
    "   #get method - how long is the list\n",
    "  def __len__(self):\n",
    "    return len(self.file_list)\n",
    "  #get  method - return the seismic image +label for a given index\n",
    "  def __getitem__(self,index):\n",
    "    #if the index is out of bounds, get a random image\n",
    "    if index not in range (0, len(self.file_list)):\n",
    "      return self.__getitem__(np.random.randint(0, self.__len__()))\n",
    "    #define a file ID using the index parameter\n",
    "    file_id =self.file_list[index]\n",
    "    #image folder +path\n",
    "    image_folder = os.path.join(self.root_path, \"images\")\n",
    "    images_path =os .path.join(image_folder, file_id +\".png\")\n",
    "    #image folder +path\n",
    "    mask_folder = os.path.join(self.root_path, \"masks\")\n",
    "    mask_path =os .path.join(image_folder, file_id +\".png\")\n",
    "    #read it , store it in memory as a byte array\n",
    "    image = np.array(imageio.imread(images_path),dtype=np.uint8)\n",
    "    mask = np.array(imageio.imread(mask_path),dtype=np.uint8)\n",
    "    #return image + label\n",
    "    return image, mask\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "j-gAE-krosrZ"
   },
   "outputs": [],
   "source": [
    "#train image +mask data\n",
    "train_mask = pd.read_csv('train.csv')\n",
    "#depth data\n",
    "depth = pd.read_csv('depths.csv')\n",
    "#training path\n",
    "train_path = \"./\"\n",
    "\n",
    "#list of files\n",
    "file_list =list(train_mask['id'].values)\n",
    "#define our dataset using our class\n",
    "dataset =TGSSaltDataset(train_path, file_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "OGtoSWhFowzf"
   },
   "outputs": [],
   "source": [
    "#function to visualize these images\n",
    "def plot2x2Array(image, mask):\n",
    "  #invoke matplotlib!\n",
    "  f, axarr= plt.subplots(1,2)\n",
    "  axarr[0].imshow(image)\n",
    "  axarr[1].imshow(mask)\n",
    "  axarr[0].grid()\n",
    "  axarr[1].grid()\n",
    "  axarr[0].set_title('Image')\n",
    "  axarr[1].set_title('Mask')\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1007
    },
    "colab_type": "code",
    "id": "uofrD1edoxv2",
    "outputId": "1fe6848f-740f-4220-ada1-372eb51b1206"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fyOMy0lf5XTN72b9YZcchvodOp5MNnF6vl61A7Pf7WRv9fj/zhV66dCm7lv2f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\n/wfsvKcMFypbUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0be43aadd8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PUkge7YOlhB6q5oRX0zmJDV4152+l0rJ+QpXuaWNjo6aS86VZesG1heB2cHsV\nuR0mnUAgEFgTtCoGlb54TV9Rfgm5cMH51Cw6hOgI45eTalZJmqEqNh6Pa19X60NJAvBefJ5DaYvS\nSGlxB38flEhMemAcMp1GTMrx0hslhpKKzbZLzsLZbFa7fz6Pkirvx4ZqN6tJUsVmpAl/L8U8NyXc\ntIXgdnB7Fbnd6gufg13yYPuBLP3WNEgkJUnDKAgf3mZ9YBibVFcRqeqVbGklVZT2Q29rbFK1SXJG\nA5SiLXwEBcegVCvFT9JSLQ/2lwtms6xryaZLcJ9/fuwLV0Oy48bjcbXdNDF9tIih2+22/sIPbge3\nV5HbYdIJBAKBNcGhJfyU0jMk/RtJU0k/KumvJL1B0oakj0p6Zs55t7mFc1/j6XRaU6FKqdc+kqFJ\nVSt9eefzeU2FMsmAUkyn06n1oZRkQieXj76w69BpRZhUweiJXq9Xk3bowGK/2H5TXZFSFUE6e7zD\nkOPKZJKS+kiJgunsHIuS849SE1d9aioxwLH0i3OUJGL2nffNNVQPg+B2cPu4cvtQL/yU0nWSfkzS\nIyVdJenHJT1V0stzzm9MKb1I0nMkveJ87VAt5Y0avN2vNBGofvpjSlEHPrzNwHomTAihKuZtqt5O\n6ydTU4QFbX+0p5LwdsxkMqnZKvkCYL8YpsdJxcJcdrw3MTBczY+ZV825367J6/HFQRW1FOZn7dDu\nzJcEk5FKtmm2z1BCX1L3YhDcDm4fV25LhzfpPEbSO3LOp3LOH805f6ukmyW9efn7LctjAoF7GoLb\ngWOLw5p0HihpO6X0ZkknJb1A0gmoubdLuvGgRkpf/aYkDapKLP3KRAf7286j178Um0v1r9/v19rn\n9alul1LK6RCjZESJjOonJT62bR59Sk80CWxublbHe8nDxoBOLkoGdBTxPgivgkr1lZOoRjPywiew\n8P7sGLZL8wDvxUuRvG7JUdXr1RfQsOfW6XSKx18gHqjgdnD7eHL70C/8jqTrJH2dpM+U9LvLffz9\n4EYaCGn2PiaSbG5u1tRSqmBMdLD9LDY1n58rzHT27NliVIGvjUGVnAWmSjZVEqUUVeFD1Aw+8YPZ\nfSS29Xdzc7OoMtM+SbVwNBpVpOSycZwsHMe9vb19dUnOnj1bkY3jW7oXPy7+mJIN05sneG6pyBav\nxfOYiEOV/BAIbge3jyu3D23S+bikP8o5T3PO/yDplKRTKaWt5e83Sbrt0L0KBNpDcDtwbHFYCf93\nJP1CSuk/aqH2XiXp7ZKeIukXl/+/7cCLu+L/0v7KcwaqMr6CHZ1DVLO4344fjUaNX2lKNbafqhuP\naVKrvPPH3yv72Ov1avVAKOWCkOGxAAAbvklEQVRR3Wdf7Rjfr5JKe/bs2VpfKHHROcT2ffw1x4JR\nDT56gREcTbHjBkqc3kFn12e8OO/VmxNs/+bmZs15WIp9v0AEt4Pbx5Xbh3vh55w/klL6NUl/stz1\nnZJulfT6lNLzJH1A0usOaoeqpYG2T6qfVLl2d3drKpGFKW1sbFTFo6g+c4A2NjYqeyKz9fykoxpn\n6PV6tb9pL7Xf+cA40ak6lyait3M2heDZvdLmyHofVNP39vaqpfaGw2FtgtEkwNA4bttxtu3tySVb\nqVdzOfl8+Jy0f1LY9u7ubs2cwPKwjFzh4tgMMTys2hvcXiC4ffy4LV1CHH7O+ZWSXul2P/bQPQkE\nVgTB7cBxRaulFUximc3OpWczGoBfTjpvvHREFdGOoRREyWVzc7NWQpZOEqZWl2KOGbdL5xCTQeh4\nohpt97Szs1OrHGjnnj59WqdOnZIknThxohYN4K9j7ZtUQudQp9OpJMTBYFCVZx0Oh41lVanK0pxg\n51l/2QalFy+ZlGK0qaLyOr7vhhMnTtRU45K0w5h1SoXeJNAGgtvB7VXkdrs1ZJdgIgLDxqgGkfAM\nz+Kk8IsW8+FQjeU1aH/koHLScWKWEiPswXCyenucXfPs2bM1lawUgUD1em9vr1bvw7Y3Njaq7dls\nVrXPczc3N2shfnwJccwYNujD8SaTSS17k8dSzbRjfAQHX1i0QVsf+fLh9Un+4XBYG1c+S77IrM3x\neFybYG0iuB3c9tdvk9tRSycQCATWBK1K+KaSMa3a19qgJMP9JfDrd+bMmaLaKZ37wlOl5JqXvlZF\nyWFCyY3qHFVBA6UzftFPnDiha665RtJitSJzbF199dU16ejEiRNVv+hcoiPOpAGf/HL69OlqP0u1\nsg06Bn088Ww2K0o+lAI5LpRkmAbOeHSqpH5MS+uN+nhumir8OEv1hbLbQnA7uL2K3F6Zevi0V5VI\nz3Kv/hgbbNalGI1GNbsoiU011ojFTDhOIu/Jtwc6GAz2qW7dbre2j8k3rK9h6ud1112nq666StLC\n/mmhZltbWzXbKaM2qI4a9vb2qnNp75tOp7XoCE5Y9pklXH10AlVkEtjXKeezLIXscZy8SYL9KoUE\n7u7u1iIsGEpXCiXc29urTaQ2ENwOblvf/PgRR83tMOkEAoHAmqBVCf/MmTOS6l5/fsUZvTAajYox\ntfz6+UWQTXqYz+eVVMGFm/nVpbOM6e1cXIGqFNVefunpoec+OnvsvK2trZp6a/2nijqfzysJZ3d3\nt6g68m9KIYwKocTHPnS75TodlAiZck/py+6bzjQvSdE0wAQWjk/JVECpjZEl7E+/36+VzmWVyZK5\n5CgR3A5u29881va1xe2Q8AOBQGBN0KqEz/jT7e1tSXVpgPYqL1Vwm+Fn5iybzWZVmz7MysCvPle1\n95lttK/yS864XWlhe2ToF22S9oWezWa1tHA75u67766cUD7OmuFnlKY4BowtLklnXvLguNo226fU\n4WOk/bj42OSSPZohgLPZrOaIYx85vgxR473wXvmcaK/1hbKOGsHt4PYqcrvVF/61114rafGAzMHT\n6XQqlc9PiqZaH6UkDam++IHBLzhgYJsk9Hg8rn4zh5RUTwW3B7+7u1sjEyMpGPtsJKMqf/bs2VoE\nhT1USx+XFpOFsbwlVZ4Ot/l8XkvB57m8D4NPrrHz6MwqOR15fz7awcBzfY0YOsVs/Oho9C87A5Oa\n7Np2f207bYPbwe1V5HaYdAKBQGBN0KqEf8MNN0jar4raF29vr14rminOpa8li0dtbGxU6iWz4hjv\ny4UTvCOFKmvp623nW/vWF0oYdDBROjLQ+ba5uVlt9/v96it+4sSJ6joMnfPhYdYvqqmz2azmlGK/\nqD6XpLWSmcGrq7afbVP64FjRucdxaioExX7xPrhk4HQ6LcYkczzaQnA7uL2K3F6J0gqTyaRSKZlu\n7QevSf1i7KpFR/T7/Zr6TLWMbXJSGPyDom2vZGc0+IUKmA5NFdz6u7W1VVTbPOFJDhKY5PBJJeyT\njQ3Vy1L0gF3PjrfrGPEmk0ltUtCeav1i5AevY+f4sWHMs48XL9kq+SyZjOTjuEvPpw0Et1X95tsO\nbtdxFNwOk04gEAisCVqV8D/xiU9IWnzNuDhAaWm00kIN9nupiNHu7m6tMJJha2urFt9q8NERBjpb\n6KWnKl3KXPQRAmyb1zdnFmN52a/pdFpU930sNqUdxiHTiVdS633ffB94zW63W1O7S/HldNz5til5\nUXrhOFGVZhw5n4eBmYmMIhmNRjXpsg0Et4Pbq8jtVmeFhWqNx+PqATPsyIcyGXzyAQlBlZnqlLWz\ntbVVrI3ByUWQFFJ9VSD2U6oTxY4x2LGcOHt7e7VwKwMnFMO2Op1Oza7HsrucIKXED754mOTCY/0L\nwMaOBGa4HicrXxYlW6l/YfC5MlqlFH1iIYhSfbEL1nFhgsxwOGw9Sie4HdxeRW6HSScQCATWBK1K\n+BaH2+/3azWvmXpd+qLziyqdi1WmU4xqkFRPC6dUUVrcgNf16mhpsYKSSt7tlpcio8o3GAxqiRaU\nZAxeMuHybXR4MSaZkQw2lt4MQMmGKfDe0cVx6XTOpXjTmUWHlz/XxogVBb1jjRIUoyZsP6soMspj\nMBjUtvlc25bwg9vB7VXkdqsv/Hvd616S6uFIDDuiKuqTPag2laIdCIZzMRyOZPIJFiSi7W8qZUo7\nJ212PN62fTlZnmuTYmdn58Dohb29vYrwnKySaqVieX+cvIwkoE3TJ/7Qtmv9sWM5vqU2ONZ+snGC\nUE22dpmos7OzU3xhUdVl1UbaSB/1qEepDQS3g9uryO0w6QQCgcCaYCXq4dNJQtWLX2Sqnz5phZ52\ng09qoWTFtTt5HCUVA2Nve71elTjD63mnm4H999EXUl1F3dnZqdq+6667aiolU7W5zdXubbvf71cS\nBCsjemcZJTH21zsSqWpTWuUzY8w1HVVMymEkAyWyfr9flM580glrytMByfh18qPtOPzgdnB7Fbl9\nqBd+SukqSa+XdFLSUNKPS/qYpFdImkv6q5zzt19oe93uuTKmTKKg2suQtr29vWIW2tbWVo3ABto8\nSQhOFqp3tOHRPkc7ql85yNpjOBcfDBMq7D7G43GVMbmzs1PZfU+fPl31l4kWPjmGKnCT+scXAMPC\n7FyuvrO5ubkvyYXhZOPxuGa3ZL0VvhiYzGJjzYUh+DLi9b1d2q7b7XZrBbpKmEwmVZs7OzuHnhTB\n7eD2ceW2dHiTzrMl5ZzzV0h6qqSfkfTTkr4r5/wlkq5JKT3+0L0KBNrDsxXcDhxTHNak80lJD19u\nn5T0KUkPyjnfutx3i6THSHrr+Rq5+uqrJdW/7hsbGzUJh9IAVSt+Dbk4QMkJRCeUd5ZRIrLt0WhU\ntTMajWq1SuyYs2fP1qQj+93gK+UxZtikHUYXMJaYVQyZvEE1lvc0nU4r6YxqPZ1MlCIZCcKEnuFw\nWFNNeS+GUqIO1/ek9EETAMeaJgA+Jzrx2BajOXjfUl1NLtU/OQSC28Ht48rtw73wc86/klJ6dkrp\nfVpMiidKejkOuV3SjQe1YyVk9/bOLUhMMpHkPuqA5Vy5ULE9HBaP8gNtJOBE49qSJDfPpT1uZ2en\nZnO0/2nDpBe/FCpG4vkCUwxvI5l5H+y7TbT5/FyWIq81GAxq7Rv4gmE4HvtGmyRJb9fs9Xq1sDED\nF7W2cbBjaKPlROR1+extPyf3ZDKpFSOjql6KaLkQBLeD29Lx5LZ0SJNOSumbJH0w5/xZkr5S0i+6\nQ1ajclUgcJEIbgeOMw5r0vkSSW+XpJzzX6aUtiQxG+AmSbcd1IhV/9vZ2dEdd9whqa7CbW5uVlLN\naDSqSUHmEPL1O+hAMrAeRqfTqUkt9kVljYrRaFTz6JciJVjNzkD1rNfr1aIEKPnYl7vX61USV7/f\nr/bzPqj2Un3udDo1qYamAko5dk/D4bC6J8b4UvojKEUwRd7aG4/HNVWbkSKURmi2KEmUlML29vZq\n64qWnFiURBlfzogPOssOgeB2cPu4cvvQL/z3SXqUpF9PKX2mpFOS/jGl9KU553dJ+npJP3tQI+99\n73slLUh49913S6rf9FVXXVUN0mQyqRGdtbD5UG17MpnUiG2D5EPeSiqXt+HRG2+gClgKmet0OrUw\nN/bPjqEqKp2z+3IcqErzWkyE4TEsY8toAG87pkrOSefHcT6f11RU2oJZB4R2atZSt+PH43EtaYYm\nCYafleyrjMJgZAXvlaruJYZkBreD28eV24d+4b9S0mtSSr+/bOPbtAhde2VKqSvpT3PO77ikngUC\n7SC4HTi2OKzT9rSkpxV+evTFtPPhD39YUj3pgo4nSi9UgVghkMkQ/LrzCynVY2+ZFEOPvmFzc7Pa\nT+mB9UFKX1pKT4y/prTlE19MkhiPxzp58mR1fyUpiPdK6YxqJ6UzRjLQOUQV1CcD+VhvOuiaknDY\ntv1tx5sk6GOSGWFhx49Go+I9UaKlZEWJeXNzs+gIu1gEt4Pbx5XbUsuZtjbYPiTLwG0me/BBeW82\n1SBGFZitznv3beC3t7drqrTt9yVb7UFwEpUmFtVJhopxseZ+v68TJ05IWiSkXH/99ZIW5GAyBkO+\nSA6GfBmYiLOzs1NLhLHx88vfUQVmGJu15zMUbdvGlC81gnZLHyVB8pfqh/B4Ynt7u/ZsGIVBe7df\nHemoEdwObq8it6OWTiAQCKwJWpXwr7vuOkn1BZqn02lVaZBxy1zYYD6vL0pA1Y5fRS6KwNV37Avv\nK9JZm6z6x/0+LZ2p3f5Yev1LyRR2H1R7uU0JjtEZlF6Yms/4a9u/s7NT3SslD0ow7BMlK4syoSTi\nE0+YQENHFdVSe66DwaAm+ZRWDuLfPi6bkg9NDxx7u6fhcFiLZW8Dwe3g9ipyu9UXPu2TdhNnzpyp\n2QJt4BnJ0O2eqz9B+xkJsbGxUbXpCWDkGwwGlcq8ublZq9PBUrBUpQ20M9r1WcvE2wdZx8MIdPbs\n2eo+Tp06VUVz3HHHHdUx4/G4WA+cBPUTh/tLKqL3+nOiUU2237kSEMeRdlODL0lrSUdMYPHZkIyC\nKPWFZYV99IL1jeaJra2tS45muFQEt4Pbq8jtMOkEAoHAmqBVCd8STObzuU6dOiVp4eBhNAKrCNrX\n1q+mw68ov8D8cloc8Pb2dpX2vrW1VamFnU6nlsJdKlHrowq8M47OFFYXZF/otDp16lStxshVV10l\nabEANh13lEh8pIHdN/tq/drc3Cyqi5TyvPPIq/IcX0oWNA1wDHyiCh2KdMRRmmN6O6VPk1ApXbIa\nYVPEAlPU20JwO7i9itxu9YXPOiFUg7goMh+w3ehwOKxUVyOytD/ywR7OiRMnKpsqJwIHmItNM0OP\n5PbLp3lCMXKAdlmq6aPRqLrvM2fOVKruiRMnqpfEaDSq7ot2Qya2NEVbcIIMh8PaykGlyAc/0fmC\nMXAC0RZMsI+0PXIMDAw/Y3giJ8hsNqv1naqujQGfR6/Xq42DRYi0heB2cHsVuR0mnUAgEFgTtCrh\nG1g2linT29vblerDhXz5xaOjg5EJdr4kXXPNNZVUMZ1OKynEq53mCGPEAFW0puqC9kXf2dmp+kXp\nqSmq4u6779aHPvQhSdJDH/rQWtJMqd6JV+dKsbxUBcfjcbEWC6UNqqOUKijtWV98aVu7PpOFfJ9K\nkQlSWbKiyuzNBqx5YqATr9vt1p4xU93bRHA7uG3HrgK3W33hMwORNS9MZbn22msrkrFgU7fbrak1\ntp/hZP1+vwqB297ergbp9OnTuvPOOyUtJoX1wdfDoLrGwTfQzklbbClb0Yd/GYHuuOMOvec975Ek\nPeQhD6llBfpEEGl/lECJ5FxFiJl+nNAlld320+YoLSYxoxtsm6FhnDjEbDYr1hJnlIJ0Tm1n1iYj\nL+y+rF8+csLa5AvOQu/aQnA7uC2tHrfDpBMIBAJrglYlfJMutra2qi9hp9OpnFAnT56sqUt0XJg6\nzJhg/yW29vf29vTpT39a0iJKwKImGMvrU6Xp4Ckll/DLzwqBpVomkooOo9tuu0233rpYSOmJT3zi\nPknF+liquEeJYTqd1uKZGSPdFPFg8LHClLJ8X7xDqhQ1QhWYKx0xCoNj7dPfqSZz247Z3NwsVpak\nxESzQVsIbge3V5HbK1FLZ3t7uwrbms1muummmyTVPeEeNhi0W7LmBG2L3W63UoPuuuuuWk2N0sMk\n4Zic4rMgDSQQ1bCSekbccccd1WQ9c+ZM7QGX1G4fFlbqgycEV9ApZW36ScOaLna+bQ+Hw1pCUYl4\n3O+jJJhNyheWvexo0+XLiFmenvy8LvvcduJVcDu4vYrcDpNOIBAIrAlalfDvc5/7SJJuuOGGWiKC\n7T916lRNxSmt+7m7u1tTy+icsu1ut1uMQ2ZsLtVYqp+TyaQowTBigFISHTnW342Ncwsn+JK0D3nI\nQ/b1xSe+0IFWSj/n8fa39bG0Dir3+yQcL3UOh8PqWK4sxOO8tFoyDXC//Sbtd9ZRNWYcuUlEvA8v\n6TB2u20JP7gd3F5Fbrf6wr/xxsVa0FdffXUtW9Ae/Gg0qsi/sbFRJW9I5yYIJwXDzOjZ5go6tNUx\nbEuqr8TTFInAB+IfDu103N/pdGrEtvMe8IAH6IEPfKCkRdQGEzyoXjJDr2RzHY1GtVAzTmrre9Py\naiSfvz/rr8EnpDC0rHRNjmG3e66kLk0SPqOQtltOBE4iHssXCTMofeGqo0ZwO7i9itwOk04gEAis\nCVqV8E0F3NnZqaSg+XxeLfp85513VlLQYDCoqb303NNhQsmDCS8Gqnb8ivoqewbfJlVd/7X3UgIl\nilK0xQMe8AA9/OEPlyR9/OMfrznZ7Fw6pLwEUEpgoTTAaou8LqUsSbXIB78IBk0GlCw47tPptKZG\nl2qJ0FFFSZRqPcedKvPGxkZN0uUYMCKCKr5PhjlqBLeD26vI7VZnhSWJnDlzppZU8rGPfUzSws5Z\nCtvyKmdT6BUHu6T2+YgFhp0RngylY+w6VKmZRUjS2rn3vve99aAHPUjS4sXAuuZU+SyrkkThtUg4\nTvThcFizr9LOWYpw4JixJovZoKmeejti6Te+gDhB2EdOUEY+sH0WufL2ZSbJWDvT6bRmT24Dwe3g\n9ipyO0w6gUAgsCZoVcL/5Cc/KWkhBdmXXlKVPOLrW5ikREeRdE4y4WICdo5UjwDg15GShB0n7V9z\nktdqcnjZ9eiJt2Mnk0l1XUoyTK/f3t7Wpz71qer6pT5SymPyBmOuWYXRJBm7t1IJWTr3vBnA+s5y\ntqUICzqtaG6w/th+Sj5UjSnBUVLivZYkq42Njdo92vbu7m5tfxsIbge3V5HbK1EeeT4/t3qM/W3/\n22BQJfK2LqrGtGEaPMn4N8uRUv1j+6VQqfOpgAba8lgcywh08uTJ6tzNzc0amdm+gWosbX/9fr+2\nnzW1S9mDhI9CMNg+hu5xjIhOp1ORkDVJmHhCVZ61RPwLpxTh4E0Y7CM5wfrvlxK6djkQ3A5uryK3\nL+iFn1J6mKTflPSynPPPpZQeIOkNkjYkfVTSM3POuymlZ0j6bkkzSf815/zqQ/csEDgCBLcD64QD\nX/gppROSflbSO7H7hZJennN+Y0rpRZKek1J6vaQflfQFksaSbk0p/UbO+VNNbbMMbCndWTr3NaZ6\nSieXr9FRciBJ9ZKotj2dTmur4NAp1YSSY4lqKY+zPvoFLuy4kydP7hsL26ZaaE65ra2tWvKNHbO1\ntVWTGKiy0rlmoEOv0zlXB4Sx0JRQuXoTk1PoLCyVzuX1/ZhyHKmyW5vsI0Gn1Wg0Kjroer1eba3X\nJgS36whuHx9uN+FCJPxdSU+Q9G+x72ZJ37bcvkXS90nKkm7NOd8lSSml/y3pS5a/F0EPvRF1MBhU\nN0RCUBX0D4c2ttJ+nwBRCvPyalJJBZ7NZrWQKHs4JRsjMxDZd9Y490vYlcqtdjrnan2fT/U3+CQR\n1lBhaFxTAokdb89gOBzWEoTYl9Ji27PZrFZLhNEFdq3RaFSLWCiZKnwUROllR9spQ9e63W5twp4H\nwe0lgtvHjttFHPjCzzlPJU1TStx9Iudsn5nbJd0o6b6SPoFjbH8gsJIIbgfWDZfDadvkQTjQs/CE\nJzyhXc/aCuGGG27Qwx72sLa7EagjuH0ZENxeHRw2Dv90SslWWL5J0m3Lf/fFMbY/ELgnIbgdOLY4\n7Av/HZKestx+iqS3SfpTSZ+fUro2pXSVFjbOP7z0LgYCR4rgduDYolPyFhMppUdK+ilJD5Q0kfQR\nSc+Q9AuSNiV9QNI355wnKaWnSvp+SXNJP5tz/m9XrOeBwCUiuB1YNxz4wg8EAoHA8UDU0gkEAoE1\nQbzwA4FAYE3QWi2dlNLLJH2hFjbR78o539piX14i6dFajMdPSnqSpEdKumN5yEtzzr91xH26WdIb\nJf3f5a6/lvQSFdL+j7hfz5X0TOz655L+TNIJSWeW+7435/znR9SflSuNENw+sE83a8W4vS68buWF\nn1L6ckmfnXP+opTSQyW9RtIXtdSXr5D0sGVfrpP0bkn/S9K/yzm/pY0+Ab+fc36q/ZFSeq1c2r+k\nVxxlh5aEevWyP18u6WmSPlcL5+bfHGVfrmRphEvoU3D7wrBS3F4XXrdl0vkqSW+SpJzz30k6mVK6\nuqW+/IGkf7ncvlOLL/r+snmrgZslvXm5fYukx7TXFUkLsv1Ei9e30giMib9Z+8foUVqWRsg570iy\n0ghXAsHtw+FmrQ63jy2v2zLp3FcSVaNPLPfdfdQdyTnv6ZzK9lxJvy1pT9LzU0rfo0Ua/fNzzp88\n6r5J+pyU0pslfYakH1c57b8VpJQ+X9KHcs4fW5YmeGFK6d6S/k7Sdy8JeEWxoqURgtsXhpXk9nHn\n9ao4bVtPQ08pPVmLSfF8LWxlP5Bz/kpJ75H0gha69F4tJsKTJT1LC3WTH+i2x+xbtIhXl6SfkfT9\nOecv08KW+B1tdcrh0KURjqAPR4bg9kXhWPO6LQnfp6rfTwtHRCtIKT1O0g9J+uplRUTazt6sI7aT\nS1LO+SOS/vvyz39IKX1Mi2zPraWU0XZ6/82SvlOScs6/gf23SHp6Gx1a4nRhjEqlEf7kCl0/uH0A\nVpzbN+sY87otCf93JD1VklJKnyfptpzzqTY6klK6RtJLJX2tOTtSSr+eUnrw8pCbJR2p02bZh2ek\nlL5vuX1fSTdIeq32p/0fOVJK95N0Ouc8Til1UkrvSCldu/z5ZrUwXkDbpRGC2wf3ayW5vQ68bi3T\nNqX0YkmVqpRz/suW+vGtWqi1f4/dr9VC/T0r6bQWnvrbj7hf95L0S5KulTTQQgV+t6TXy6X9H2W/\nln17pKR/n3N+/PLvp2lRU/6MFuUJnptzPntE/Vi50gjB7QP7tZLcXgdeR2mFQCAQWBOsitM2EAgE\nAlcY8cIPBAKBNUG88AOBQGBNEC/8QCAQWBPECz8QCATWBPHCDwQCgTVBvPADgUBgTRAv/EAgEFgT\n/H8X6Bt28NxOGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b53d78668>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ktCqx2LbYlvGWf88Dtlf6wn/9618PYPlwTz/9NIAlgOX7SqWiBaCvXr2qJt8n\nP/nJU0mP2M/puq4RscAc43L9aDQyfH9c5Fgmt9vtagHowWCg7TPNrCym+XxuJHeISbaxsaEZi3t7\ne5p9x/5MBir77NI0NcLFONuRIyjYFyp99zzPaEdC8PglwdwjTEjFURqn0cAOh0MjAoGrBQn4uUJQ\noVDQCBUuXs0EV5xgInMo1zCJmCwQ+U7ue9pvVyUW2xbb5xHb1qVjxYoVK5dEVqrhy0n/888/rzvb\nm9/8ZmWMe/rpp9Vsq9VquutxfOtoNNJohDAMVVNiTWY2m+muzoctw+FQE0yiKNKdOU1T1ZRqtRoe\nfvhhAEvtiw9SNjc3tT+AqUUkSaJtv/DCC3jLW96ifZGDsiRJDA1EtJSTsccS89xoNNQEl2eW6+VA\nr1gsGodZoln1ej3tz2tf+1r9bRRFar5ylIX8Ox6PdW44SoETRiqVinGwJdqOjLE8h/SL47w5KoUj\nPrIsM4payL0Wi4VqZZPJxDDJuR1OkFmFWGxbbJ9HbK/0hS8D1u/3cevWLQDLcKdnn30WwNKfKQun\n1WopEPb29vT7crms33uep9+PRiOD05rDwiTTcDgcah/YFIuiSCctyzK9/vnnn9eogvF4bJycA0tA\niCk8Go20vY9//OP6fb1eP7VocaFQ0MgAppbl8m1cCJn5xsMwVFOTQ7aiKFIgTqdTw1zkhB72FXLy\nifRRTMgse1A2j01RebaTfedkF+mztM20tfzyEuGoECakYnN2sVioy4GzEVdNjQxYbFtsn09sW5eO\nFStWrFwSWamGzxVrJEphe3sbn/jEJ/QaORDyPA8vvPACgKUJd1odzYceekh3ySiKVAtizcB1Xf1t\nr9czEkwkRno6narJFcextt/tdrG9vcyqn0wmqn2xecb1OuXvH//4x/HBD34QAPAVX/EVatYzyx+z\nHnKEBVPhAg80jvF4bFQCkudoNBoGdwubzxIjHcexmvXz+VxNzVKpZEQ4AEutUdpmfg/mDOHCEBxF\nwBwq5XLZiEPmYhvy2/39fUP7FA2xVqupptRsNvX5OL0+l8sZh5evlEvnlYrFtsX2ecT2Sl/4YhYy\nr8iTTz6JnZ0dAEtO8Rs3bgAwq8G3Wi2jbJwMPIdqeZ6n7Y9GI51sPjnnQsidTgf3798HsFyY4sPc\n3d1Vc6xSqagf9eDgQPspZjEDiEPqhsMhgiDQ53vd614HYDnxTBjFC/G0CS6Xy/ocURQZtLTSTr1e\nN8xYAX+z2VT/4+Hhofo8OYTsZBaktM3UtgJgDj9LkkTHkWlxT0YmyDWLxUJfVO122zDNOVmIFx2/\nJLi6ESeqyPWu6+rLYFVisW3cE+CkAAAgAElEQVSxfR6xbV06VqxYsXJJZKUaviRdPProo7h79y4A\n4KmnntJCwY899pjBGCc75EMPPYTbt28DWGoVYgqyOVcqlVR7YXMujmPDXJRdemtrS83UJ598UjUG\n1kiAJY0ssNROxAS+d+8eAOD27dtqWrLGwjwkd+7cUW1na2tLTXNO865UKnrINZvNlGOED9xqtZo+\nd7vd1ugIpsvluGXmDZnNZoYmyDHBco1oWPP5XLXPZrOpJnuv1zM0FmYrZO2IKwdxdSFOfmHTn+mA\n2aQVrYYPFGUcgCU+5L6rducAFtsW2+cT2yt94YsJ+dBDDynA6vW6moXlchmf+cxn9LMskLW1NZ0c\nDu1iieNYzd5Op2OUQBNTibP7bt68qYN5//59XThM61ooFAyf3zPPPAMA+PSnPw1gmUDDFLYC4Ecf\nfVR/93u/93v6rGxGDodD9QMyEGu1mvFi4MLQstA6nY76DcfjsQFoJtYSAHU6HX0hMfcHA5TvKSZy\nu93WfkVRpP5RjlZoNpva9skoBva5Sr9kjqSP0ma9XjeKfEt/uOQch6fFcayYSNP0FRFMvRpisW2x\nfR6xbV06VqxYsXJJZKUa/qOPPgrA1GTe9KY36S7+6U9/WnfJ8Xismsza2pqhvchvuf4kn5bzDszs\nd1tbW7qTdrtd1crkgAtYHqJxPKxoLfV6Xc1zSZ1/9tlnNSKDKwjJcwFL0/h973uf9lFM2uFwqFpY\nt9vVgyougsEn+p+LDfFkvDSnaMvYcEWfz5W4I99NJhOdj2q1qgeHHOPMlK0nuT5Ei2QGRKaTnc/n\nRvSC/JYparl9jmdeLBaGa0PuNRqNXhGj4KshFtsW2+cR21bDt2LFipVLIueCLfPg4EB39CzL1A84\nm81U83FdV7UR5shuNBrG4QkXK+B0ZLmm1WqpL206narG0+l09NBqOp3qNZy557quHpxVKhUlnrpz\n5w4A4LnnntMDMa49ybHEt27dwjve8Q4AwBNPPGFwcEtqebvdVpbFg4MD1RKuXbtmlM7jgxzWdkTD\naDabOn7FYlH75nmeoTWdPMwCHpS+i6LIYAIUqVQqOhas+bDGyRzgHILH7IKcRch94TljvyXHJzuO\no8/H9ARMhLUqsdi22D6P2F7pC1+iF/hUfGdnRwd1bW1NzZq9vT2NCeaFMx6PdbAPDw91YPr9vk5a\nt9tVADHNa5qmRuq6JMIcHh4aPBkSbbC+vq6LDngQoyyL6ejoSA+7arWaRjXk83lNfEmSxHiOF198\nUb8XytlnnnlGIzXYvJzNZtp3pqXd3d01Fhcn5ZxW9zMMQx1jNuu5mIbwl/AB3e7urj4TF33mRcEm\nOFct4siOarVqVC6SMZ1MJkZ8shxU8QuAE3UqlcqphTiYbXFVYrFtsX0esW1dOlasWLFySWSlGv6H\nPvQhAOZOO5vNlPGu0+noLv7EE08YREYSHrW9va07HmehRVGk38/nc9Wm+NCjVCrprl6tVpU5EIAy\nBzL73clsOSkDJwdrXNDh3r172h4TKu3t7emuv7m5aZBsycFar9dTki0+7EmSROOc+cCL08xrtZqG\nmt29e9cww+WZoijS8XAcx6j1KRoUh8XJM4dhqH1PkkQ/c4haLpdTzWQ2m6l5e/XqVT2M5Bqrs9lM\nNS7mOC8Wi3p9LpfTfs3nczV1ua6oPJdcw26AVYjFtsX2ecT2Sl/4MvBRFOkgra+vqxlZKpUUNIvF\nQk3U+/fvGzUhOUlDBnh9fV1NOOYV4dNv5hLxPE9TzofDofocF4uFFqFeLBZ6PfvnJKLhueeeM2KP\nZSLZPDtZTUciGba3t/W5C4WCXr+zs6PmXKlU0r6USiUFvOu6BtOfPGuSJBoJwv1lGtt2u22w+8l4\ny4uj3++r+Vmv13V89/f3dVFKv+X+IlxTlKltq9WqLqTBYKCLuFqtGsUpuM3TFuZwODRi1uV7rnS0\nKrHYttg+j9i2Lh0rVqxYuSTyijR83/crAD4B4H8C8AEAvwzAAXAfwLuDIIg+z8/xzne+E8DSXJRd\nt9vt6kHL0dGRZiOOx2PVNg4ODnRHb7fbqklw8QguqDCdTnVX5bqRo9FId/0sy9QEXl9f1wiH/f19\n1TyYPIkZA8W0azQaqgWwKZjP5/VaLgxx//59g6FPfuu6ru7olUpFzdUXX3xRn2kymSj5VqlUMup7\nyvh1u13VMLg823g81v6cLKXG0QmAqQW1222NcJhOpwaplGhhnuep5sMsglxAo1wuG+4D0Szz+bz2\nnTVBLifHtUHn87mOE1/Dh5dnFYtti20Zl4uE7Vfq0vlBAEfHn/8hgPcEQfBrvu//MIDvAvCTn+/H\nUt/z/v37RsiZTPDTTz+t/r5yuazg5JTofD6vA3xwcKDA5kSVer1uRC/I9XEcG4WkRcrlspqO4/HY\nSLMWU48Z7zicjUPtmH1P2tjZ2TGiKqQv7KtM01QBsbm5aSTZyKL42Mc+hueffx4AcOPGDYNpUCIl\nms2m4U4QP+psNtPrx+OxAVwuYCHtsR9ZJJ/PG3VP2f8pbbCpO5lMDApbGY/RaGRUGpLxLZVKeg2H\nwDWbTWMhy/VcTKNYLOqYvQKx2IbF9kXD9pldOr7vvw7A6wH838dffS2A3zz+/F4A7zhzr6xYWaFY\nbFu5qPJKNPwfBfA3AXzH8f9XyczdA3DtpRqQ+N3JZKKaT7/f11Jrk8lEd+tHH31UzS/HcdQUDMNQ\nd8J6vW6UZONdV3ZINonK5bJqMKINSTsckywawWAw0M+5XM5gDASWUQRMwCQmIpMl9ft93aHz+bxR\nYIJNdtGCqtWqmvs3b940SK2efPJJAEvtQRJ90jTVz51ORzUx5u92XVc1pdFoZGg8IqJpsJuAU8W5\njiczM54sK8dp7EwlwJzlbAJLfw8ODrQd1mI57jxJEtVWT5rJPH9nEItti20di4uE7TO98H3f/y8B\n/PsgCJ71ff+0S/5EdG4f+chHAJjFErj4wiOPPII3vOENAIDr168bJ9XyOZfLaVLJdDpV/1273VbA\nMzUpm5o8mXEcK1jTNFXQzGYz7Vu5XNYoiDAM1ezjQhOyuCaTiUHfKkDd3d3Va3Z2dnSxVioVo7al\nRCnk83n9vLm5qVEDjUbDyBKUNh3HMcDBhSrEjPQ8T8PtHMfR79M01TGQtjlc7+joSE3ajY0NIzpE\nZLFY6P0Xi4W6AdI01ZdEqVTSeeI5KBQK+nLs9Xq66LiQhDyv/Cv9nM/nOt+Mp5crFtsW2xcV28DZ\nNfy/AOAR3/f/IoCHAEQAxr7vV4IgmAHYArB95l5ZsbI6sdi2cmHlTC/8IAi+TT77vv9DAJ4D8KcB\nfDOAXzn+97dfqh1Jt+bY0tFopObtxsaGcnpkWaZxsnxo1O12cfXqVQDL+pp88MQHTqINxHFsmFyi\nATCDXaFQMFLKZcd2XdfYsZlrG1iaZ9Kv0WikmkmWZXjNa14DYLmji/mXpqle43metjMajbSdSqVi\nHBSJNvVlX/Zl+tzPPPOMHnjN53NNkImiSLVFPqByHMfQBLn0m2gbbCKLDIdDQ5vkwzSOxWauD2l7\nsVjosyZJYrAIMvsfp5Cfprmymcx9YLqBcrms2tfLFYtti+2Lim3g1U28+vsAfsn3/e8B8DyAX3yp\nH8gAcPHlGzduqKnLJEa9Xs+YPAHHrVu39JrxeKzthGGoIW28QPL5vJHsIYBj/gwAujDX1tY0jI35\nNrhIg/xbKBT0/sPhUO+TpqkuMk4AcRzHMNX4xSAmJY+B67rGib74P9fW1tTnyf7Ee/fuGWa1mOl7\ne3u6cJgXhUPKpO8MSM4Q5P5yHU+mtuVQQgY/F8Rm8HLha8/z1ARnU344HBrcKvJSOzo60nt5nvdq\n0yNbbFtsXwhsv+IXfhAEP0T/+85X2p4VK+dFLLatXDRZKbWCxCrzifvt27c1Jnk4HGqssud5Bl+F\n7Nye56kGUK1WdVdtNBrGri+7OhdC4F2dNRgAuqNeu3ZNY4L39/dPTcVmKlf5+/7+vpqccRzrtYVC\nQb/v9XoG6yDH18quP51O9ZniOFbtiA+tWq2WHu5NJhPt78HBgWoMHIVweHioTIZXrlxRbZGf+7Q0\ncDbNuRYnm85hGBpaHvOHfK70cI4dl/lwHEc1K9ager2ezpPneeqS6PV6On6rLm8IWGxbbJv3PC/Y\nXukLX6Ig+OR5PB5r4WTmjWZeEQ7D2t7e1sEuFosKfl4g6+vrev3R0ZECazKZqEkZRZHeK45jNQvX\n19fVvOSsyVwup5PABZo5QYJFJp5N7SRJ9HR/MpkosKvVKl544QXtl/Tx8PBQ78mRGsViUftVKBTU\n/8n+TABKiBWGoZqLpVJJf7tYLLRNfiZ+Gcn34/FY27t69aqa2nt7ezqXhULBoITlhS7XnwwT5CLV\nci8mxGIfcL1eN9wGvIBXTY9ssW2xfR6xbbl0rFixYuWSyEo1fNF2xuOxHnTs7e2pKdhut41KNaIF\nsRnJlXiAB9XeOWZ2c3PToE8V03QymehOG4ahagO8S7uuqwkkpVJJ7zsYDLQ/onmx2Vqv143KN9Iv\nPmACHhRj2N3dVf4Q6ac8K5uCokEBDzSr+Xyu/S2Xy0b6OdOqSvp5oVDQAh1xHKuZWigU9DM/k8xH\nLpfT8To8PNRxqdVqan6maWo8n2gsXF1pOp2qdsRjxtSyJ2lg5Vl5PprNpsGYyFWEVq3hW2xbbJ9H\nbK/0hc+VZyRZgiMEToaHyfdHR0fqp4vjWMFXq9U0hCuKIiPTTyaNT9eLxaJB58omMPNhyMKs1WpK\nvcon5RxKJVKv1xVMYRjqfXK5nII8SRL9PBwONZRvbW1NwTcYDPRzq9XSNkulkuHDPK3qkeu6Cr4k\nSRSs9Xpdx/Izn/mMhpSxCcy+QhnHJEl0zvjvHL3geZ72MUkSfdkxCdXh4aHxkpCx4WvSNNWXV6FQ\n0LFtNBpG4W0ZAw6H4xfJqsRi22L7PGLbunSsWLFi5ZLISjV8oZB1XVdjjyeTidb0PBnHKibZYDBQ\njaXdbuvunmWZaizT6VTb5EQRPmxhs4njmZlpkDkzut2uakfj8VjNS9mhOTJhbW1NNTLP8/RZSqWS\nQRXLMbiiYc1mMzU75W+AeXDGFXTm87lRnEE0lEqlYlQUkuuvXLmi8eBMb1sqlYzUdemLjIvnecZB\nmWhEzWZTv+c+spnO1XzK5bJqKVyPdLFYGBWCRINKkkQ1n0qlYkQssPbHdLLsClmFWGxbbJ9HbJ+L\nsMxcLmdU/znNHzadTg0OCTHFmJub/ZNM9zqbzXRyOp2OUUmeT8jlezY1gQeLkxMyDg8PcevWLQAw\nIiNE2u22nvRzOTSmWK1UKvoccRwbJp/0nelT4zg2QsGY50NAFobhqb7bNE31mtlspkB8/etfrz7P\n2Wxm0NUCy/A38Udfv35d+8U+4kajYURnMIcLj53M8Xg81nbYbeG6rpGIwwkvnCXJvOxc2JuzNlct\nFtsW2+cR29alY8WKFSuXRFaq4UtSCQA1+RzHUXPx4OBAtRzmsajVamrWcMr4YDDQiIXbt28b/B1c\nr1N2Ua4lyqf04/HYSH3meFi5Zm9vz6gxKn+X/jQaDT2sOzo60h09DENDkxEtaDab6fdpmupOXqlU\ntE1OaeeT/izL9Jm41ifzgERRpEkobCa3Wi1NBrp//76h2UjfZT7a7bbOR7FYVK1ve3tbNZlcLqf9\nGo/HhlbDySliGrN2xoeBzBniOI6hwYmGVq1WdZ6YTyVJEtVuVyUW2xbb5xHbK33h37lzB8ByILny\njvg5J5OJDgaHivEpNfvm2HTtdDoKoDAMdaIODw8VBJPJRJND0jRVgEo/AHMh1Go17cPdu3f1t5JU\nwqfslUrlVL6TNE21j1mW6WJxXVcnm6lRmWOk1+spORZHDEynUyMDkH1/Ao7BYGD4HOWaYrGoC4AX\nnRS9rlQq2vZsNtMx6vf72pednR3D5JR+Mfi5hN7Vq1c1gYYTYlzXVRcC+0XZ3cDmc6lU0vGuVqsG\nz4n0bVVisW2xfR6xbV06VqxYsXJJZKUavphTbHLyTs+m0nw+N06/Zccbj8e6K8ZxbMTySrGJcrls\nVJIRTebw8FC1r1wupzvtyRhf0VRarRYefvhhAMuiy1xcGfjsikO8Q8v3XGgBMCvxSDvMmud5nmF2\nS385FX44HKpGtrGxYRwyMVWsjDHHBE+nUx3XWq1mREoAwMMPP6z8JVEU6fg2Gg2jXihXQ5LfVioV\njSbhA6+NjQ3VsnZ2dvTgbDAYqEY2m82Motbi5nAcx8CH3GtjY0NdGFmWqem/KrHYttg+j9he6Qv/\n8ccfX3aCTuu5yDITPq2tren3rVZLJ2F3d1cHqVar6TVHR0cKsmq1qsks9Xrd4LpmAHHIF4eOyTVR\nFOlkcsk3jkA4LUSOxXVdBbzrumqWcgFqrnBTrVaNxScviaOjI8NvyNEDAmxecJ1O51Sa18lkYiSK\nyLMwV4vwrTC3S7PZxCOPPAJgueBkfLe3t091N+TzecO/LP7og4MDjaRI0xS3b98GsHypcWYkL9bT\neMXF1wwsFxRHlaxCLLYtts8jtq1Lx4oVK1YuiaxUw2dTlJM3uECCmFnXrl0zignLTri7u2toL7Kj\n7+3t6SEMp5Nfu3ZNtYM0TTWeuFgsGkkVcs1kMjGKREuVomazqaYmc5Pwzi0aAx/MsKZWLBZVC5pO\np6ptcf3Lk7wZXHRa+sj8Hhy1wenfJ2O3RQsKw9CoGSr9kb57nmckuMjzNRoN1YJqtZpymdTrddVK\n+aAxyzJ1A4xGIzVp9/f31T3R7/f1vp1OR83xcrmsfZzNZobGJ8/hOI4R3SJa5KrEYtti+zxie6Uv\n/Le//e0AzJP4YrGoEQAcVtVut3Vg9vb2FOSz2UyzAhuNhppZg8FAr+/3+woIz/P0+8VioX44jo5I\n01QnZ29vT0F35coVoyoQZxICJl/H4eGhTt76+rou1lwuZ4CYS51JH4vFovrsOOSMebQ5hOvk4pIx\nKJVKRjKJPOPJwsxcvUg+s2tAgFcoFAw+EhmLra0tfbm4rquLgkvoMcVrmqY6f1wJaD6fawhjGIYa\nIdLpdHQRhWGoL5XZbGZQ4cq48ktlVWKxbbF9HrFtXTpWrFixcklkpRq+7NDValVNtWq1auyuoqXE\ncaw75O7urhZxiONYr2m325owwodAo9HIKDjMbHhyr06nYxQi5mQSPnSTfrbbbW1f7rmxsaGHN/P5\n3DDTpY9hGBqMgrJzcywx09+yltBoNLR9x3EMc1WuSdNUNYYrV64Y0RFcvFm0Jo5blnsDZgo3F1CW\nvvf7fe07xwm3Wi2NRmi32xoFsbu7q8/UaDT00LJerxsHfcyhwnVh5TMXiZjNZlqTtVarGX1eNVum\nxbbF9nnE9kpf+DKpURTpSXSj0VAAcVHk/f19LQnX6/X05Pz69eua6NBut/VUfG9vT/1eaZoahYUF\nfGwqra2taX/y+bzBky0DzORJV69eVd+s+One+MY36n2Ojo6MhBEBQbFYNJIx+P7yfaVSUXNxOBwa\noGQCKF7o/L1cz9XukyQxikRL35MkUZClaar9lPYWi4WOCyfQjMdjjUYYj8fa39lspmOQy+W0Pc7q\nLBQKOt9bW1saHbK3t6cLmjNCsyzTRZzL5bS/vV5Px7jT6ejCbDQa2p9VicW2xfZ5xLZ16VixYsXK\nJZEza/i+7387gP8BQALg7wH4YwC/DMABcB/Au4MgiD53C8Bb3/pWAEvNRA5vOGU5iiI1dV944QUj\nPll249u3b6vGEsexHqowO57jOEYxAdE8ut2u3rfT6eguPZ1OdSfnNHI+uLp69apqU1KjM45j1bz4\ncGUymaiJzKfsrVbLYEQUzWSxWGh0BMc8LxYLg4OD+8hp+tLHyWSiZiz3nc3D8XhspItLP7lQh3wX\nRZFqRJPJRF0Dd+7cMcxSGQNmZeSaocyzUiwWjcgO6dtkMtHx5SgITgDa3t5Wbfjw8FDH7ObNm68o\nksFi22L7omL7TC983/e7AP4+gP8IQA3APwDwLQDeEwTBr/m+/8MAvgvAT36+dr78y78cwNKc40GV\nkLB+v6+Tw5l1o9FIw6Zu3bqlg310dKTmGgOOgZdlmf6/4zhGBRsBfz6f18lvNBrqtyuVSgbJlURQ\nfPzjHwewJGiSa4EHwOLSdsADX2I+n1egMomS67p6Qt9ut/We+/v7+tswDA2TUkAzHA4Nc5gLZfPL\nRhZaFEVGmTk2/QGzIs9gMNCXTqvV0n49++yzRvam9Gs+n+v37XbbeFamuWX3B1cuksXImYyc5FIu\nl7V9DpPjLMiXKxbbFtsXFdvA2V067wDw/iAIRkEQ3A+C4K8D+FoAv3n89/ceX2PFypeaWGxbubBy\nVpfOLQCe7/u/CaAN4IcAVMnM3QNw7aUaeeqpp5adKBRUeykUCqoFHRwc6M4Zx7FGLywWCzz22GMA\nzEMPrvdYq9WM2FzRKrhSTZZlupMzx0e9XtfoBKZ2zbJM++k4jvbhwx/+MIBl0Wnf9/Va1ljEZOeY\n5DAMjb6LuVitVlXD4iiBJElUk+FojCzLdJy46tB8PlcTsVAoqPbH6dzlctkwa2WcWJvjOGtpu9vt\nahv9ft/QUkQjKxaLqt12Oh3t42g00v7n83k9IMuyTMeAKWRZm8rn80bBDenDaDQy6raKRnsGuQWL\nbYvti4ntM7/wcwC6AL4RwE0A/+74O/77S8oHPvABAEtAsu9RgHd4eKgD0+/39fOtW7c0aeXevXsK\n/lwuZ0wgZxEy4GVCmNej1+vp9d1uV0/U5/O5QcMqoOh0Onjd614H4EGY1L1797TtLMt08bmuq20P\nh0O9fjQaGYkhzK/CxYwFtO122wjxk4UGQMFcKBSMCvccCsYJJ1x1SADNwOKXC1fkkfGtVqtGoWkZ\nI8dx1G+ZZZkCnkvh7ezsqKnbbDZ1DIrFouHTlfYLhYLByyJ929zcVC6U3d1djWJhF8IZxGLbYvui\nYvvMLp1dAH8YBEESBMEzAEYARr7vV47/vgVg+8y9smJldWKxbeXCylk1/H8L4P/wff9/wdLsrQH4\nHQDfDOBXjv/97ZdqRA5J+v2+7uKTyUR3vOFwqKaS67rKNuf7vmojk8lENSjRFgDTFCwWi0bRBeYB\nYdOU08WFV+Tw8NDgz5AdvtPpaNUfibXt9Xq6o3NEBp/Qz+dz/T6OY+3jfD7Xk/hOp2OY5lykgs1d\nNvnYHBdNhSv3xHGsz801Q3O5nN6L63TKd91uVzUTToLhz+Vy2UjEEU2qVCoZccsyZ6VSyeAwEbM+\nyzKjmAYn4sizTqdTnY9KpaJzUKlUjKQVZlN8mWKxbbF9UbF9thd+EAT3fN//lwA+dPzVfwvgcQC/\n5Pv+9wB4HsAvvlQ7ArwbN27ogzINq+M4+qCPPvqo+hCvX7+ulYO4Cs3u7u6DB6NK94VCwUjA4LAw\nJmOSgS+VStq3yWSiJjBHHoxGI02wkEVxcHCgpiibvUwwxXzhbJrHcazPzSFfXEqt0WgYFesFcIvF\nQtvv9/uG6S/94YXDiTXcH85KlMVfr9dPzVYslUr68uJwsiiK1NSt1WrG9/KsjUbDeEaOWJD7ym8A\ns6LQfD5Xf3en09ExqFarOg+cLPNyxWLbYvuiYht4BXH4QRD8NICfPvH1O8/cEytWzolYbFu5qLJS\nagXRfBaLhe6oWZapVtPtdvVE3fd93SHTNNXdnQ97mIuCNRbmyUjTVO/FhyqcAFEul9VMbjQauuvO\n53M1O3u9nmpTojF97GMfU36NtbW1U7kzisWimnknzVKOu5XPzNPhuq5qXlyAgWlVOfIiiiIjEkTS\n5NfW1vQ5mEOl3+9r3+TfOI51XNhMz7LMYC7k5BsZl8ViYcRfy7PW63XVjnjOkiRR9websYAZuSF9\nA2BQ7QpWPM/TdlYlFtsW2+cR2yt94f/u7/4ugOXkySBtbm5qhMCtW7fUzCsWizohBwcHOjDr6+s6\n2Pv7++oL5XC1tbU1bZ/pZOU6/hdYLijO+uNqPWJO9Xo9ve+tW7cALE0vKV7NoV2DwUDvDzyIEuBQ\nOF4Ue3t7+nlnZ0f72+l0DB4Uab/Vahkg54o48hy5XE5fBgcHB5q8USqVDD4VMbHlpdPr9YyFwotC\nPjOAuXi19EfGl6sPyQvOdV0dg36/r+1UKhWjGhO7JGQBcqRGPp/Xe5VKJcM9sAqx2LbYPo/Ytlw6\nVqxYsXJJZKUavphTtVpNzczNzU2NQ242m7pzspbApp3neXjuuee0TdlFudBvFEX6/2wOZVmmZhNH\nQfBuz7vreDxWTeLw8FAPvKRAwpUrV5QfJUkSNQtPJm9IfG0cx0ZChfR9MBgY1W5EG0jT1ODsEG2D\naVgbjYa6E1hbjONYNRsuqOx5nkEFK+Mhmkmv19OxyeVy2h6n9DMnSj6fVw2n2Wyq1saaWbVa1fvX\n63W9F7MbclIO12ctFAr63KwFJUlizOGq6ZEtti22zyO2V/rCf9e73rXsBCVI1Go1HfDhcKhg5qLJ\n7D+L49hIgJCBYQKmwWCg7XA2IPsf5W+ASYY0n88NoippnylUpS9cXT4MQ+N3cm0cxwrCkxl3Aohc\nLqeLZWNj41Qgym/k/mxGMq+IvFTq9brh02UyK/aFyjzItRyO5ziOzkO9Xtfn6/f7RiFmzoCUZ+Jo\nBAa267r6UhmPx4bvlBNeONGHqXblXkmSGP5V9oWuQiy2LbbPI7atS8eKFStWLomsVMOXCIAwDFW7\nqNfrapryLg6YNTLFTJ7NZvpbPpxaX183TFRpx/M8o8C07K5MM1sul42kEfm+1WoZWoKYcqL5NJtN\n1QzCMNTEicFgoDv30dGRcpkwPStXIqpWq6ptsIbISS65XE7HaT6fG4dlfCjHhaylD3t7e3rf6XRq\nuBPkmeTaOI6NuqNyLR+IjUYjo8qPCDMXMgVAFEV6/263q31kylvWlDimvFar6W85CoNdFRx3viqx\n2LbYPo/Ythq+FStWrFHmax4AAB0ySURBVFwSWamG/8lPfhKAmRHHXM+lUsngxRZfHhcAiKLISL2W\n69vttu7AxWLR8Icx8RTHzMoO3O/3jdhi0WxKpZJqHsPhUD+L37JSqRi+SIkrHo1GunPfvXtXsyZ5\nd+dDI0635lhe6QdgxndzrHKWZXqwxYdl6+vryoA4n89V29ne3lbNZTqdGiXkREQLYl8zax188DQe\nj40wOtHIKpWKwfkuc5bL5XDjxg29j5BEcT1SzrYsFouGxifXpGlqkHut2odvsW2xfR6xvdIXvjxQ\no9EwDjQE2FmWGUWWZSDTNNXPDLhms2kcDskiajQaCvLZbGZU7mEGOwHK3t6eUWBC2nddV0/gR6OR\nLgo5UOEKN4vFQsF248YNBe3du3cVcPV63TAXpS9hGOpn5gBhpryTxZ1FZrOZXs+x0LlcTt0MR0dH\nWkOVeTny+bw+n7RdrVZ1USRJYkQ6CNUv84TEcawLhOej1+sZxaVlTLmQ9nQ6xb1797RNedkUCgUj\npZ65W5i3RCSXyxkukFWIxbbF9nnEtnXpWLFixcolkZVq+FLKjYmk+PAmTVPd/dI01QOhwWCgZuHB\nwYHuulwXk3/reZ7+ljWMXC5nhH+JFlSpVNRkHQwGhgnc7Xb1e9l5JQZ4bW1NtZowDLWP169fV42p\n0+loXzjWlrPvwjDUzxzLG4ahoXFx8QjWBriup4xBlmXan/X1de3PeDzW9rMs0/5z3K88JzP1hWGo\n979y5Yrek78HYMyTjBNrvZPJRA8GmWRrOp3qGHieZzAKinC4H6fG5/P5lbt0LLYtts8jtlf6wucY\nVZkE9v3NZjM1lUajkfoHOQJhOBwqIOI4PtVcnc/nxmTKhGRZZky+fN9ut40KOmLSnVwUAmg2qQVU\nk8lEzdtut6uLbG1tTcFRKpUMc5/9hlxrVPrORSrYlGd/IgOiXC4bTIpMqSumv+M4ukDCMDReKsDS\n7JVFHEWRApj9ltVqVeeS63UuFgsj8YTdB7JY2d2QJIma9Uy1K3+T55PvOQGFi2NzDPOqxGLbYvs8\nYtu6dKxYsWLlkshKNXzRZJjQaTKZ6OfBYKAaw+HhoRHBIKfWk8lENYnNzU1sbW1pm5IBeHBwYNT9\nPK1gQ6lUUs2jWq3q7j0cDnWXrtVqenBVq9WMupdyT9bs5P4cS9xut1WT4rJnbOoyW1+hUNA0dy6I\nwQUu+KAoiiJDIxItiIs3AA80CNd1jRqcHO8rfRRtp9/vqxaUZZmO19HRkWqNnU7HMLX5MEsiMTzP\n0/tPJhNDe+Gx4fJz8qysQXmep79lE5gPR1clFtsW2+cR2yt94Ys5xUx9fBI+Go0Mv5dMXrFY1AGo\n1+tqwj388MM6qPv7+wY9K9f9lN+Ox2OdcNd1Da4SWYwcisWn6MwYKMLJIDs7O/p8R0dHOqndblcX\naD6fV8Bz+Fc+n1fQVKtVvaZSqSiwXNfVPnLFHw7zStPUqLjDEQ6cCMOp/1yFSdpjvhH5nqM2dnd3\nNaqh0+noOLKLoVqtGuPFyTcyT6VSyQjP4ygTrkwkz5TL5fQlMRwOdSFUKhX9flVisW2xfR6xbV06\nVqxYsXJJZKUa/qc+9SkAyx1VzKM4jnW35PhT13WNkl+cYHL16lUAyxN1+c3+/r6ane12W3ds5h7n\nFOcsy4y6kVzAQnZU5qtutVqqVUhCzXA41H7VajW9/4svvqiaWrlc1hjmarWqGkAURacmp/COnqap\nagkcrw08IJsql8uGFnQaJ3mapkYsr2iO8/lc25E+cvQCl7ZbLBbGfHCsslwfRZFRnIOLYMj1TNrF\nmi5znHNCUbFY1PaLxaKa3hsbG0bq+qrZMi22LbbPI7ZX+sKXRIRKpaKDwTwWpVJJv+dMw1qtZkwq\nF3qWaIft7W0FWbfbNShRJQKAixswZwZnBk6nU4MhjxeFmK+y4EajkbbdbDbVXL17964+U6FQQLPZ\n/Kx+3blzx0jYYN+qgIkjL9ik5XFik5aZ9RjcnOmXz+eVF4XNS0lI4XAyDjkbDodGlALXF5WFNZvN\nFMxhGBosglxIgv2yMjblclldIVEUGS8mWVBJkujCbLVa+j37RVclFtsW29If6ct5wLZ16VixYsXK\nJZGVavicKi4aSKvVUlOGDyfYLC2Xy8YuJ5rBvXv3lK8ijmM1h/lQZzabGQkv0uZ0OtUCD2zGTSYT\n3WmjKNLd+8qVK9pP2ekdx1EtiLnPWTvqdDpGXK9EXty9e1dNyslkoodilUrFYC6U/rqua0QjiDAr\n32KxMCIZ5HtOrEnTVM125mWRPmZZZkQvMJcIH6xJHw8ODvQajqrgw7dSqWTwh3CCiZi69Xpdx2Ay\nmWgfK5WKXsPPXq1WDRbIVYvFtsX2ecT2mV74vu/XAPwSgDaAMoB/AGAHwE8CyAD8cRAEf+Ol2hFA\npGmqD9dqtXRiisWiEZ7FvjQuAsxkSZwZyOaoXMO+Ol6Mi8VCJ595SDjJIQxDBbfnebh586a2Kd8x\ngMRXyEUqWq2W+iqLxaJhyrP/VQA0m830uUejkZqFTKgkYwKYpuBJ00/GmH/HyUCu6xohc9JHaTuO\nYzWjK5WKwYMi1/R6Pb1PPp83okm4iDMX0mY6YLk/c5swXe/h4aHh55QICg6xS9P0zBSyFtsW2xcV\n28DZXTrfCSAIguDPA/gWAP8bgH8K4PuCIPgaAE3f9//zM/fKipXVyXfCYtvKBZWzunQOALzx+HMb\nwBGAh4MgePz4u/cCeAeAf/P5Grl16xaA5SGJ7Myc1hxFkaEBMFWs7PSsHXHCRLvdNupDct1NuZ53\n/eFwaHBUiGZQLpd1R10sFroz37t3T00xjpvmVHTRoFzXVY1hOp2qJsNxtxyBsLW1pVoTl1Jjs/Mk\n0yBrJ9J+kiSqeSRJotpGs9k0UseZf0WEE0Y4UYdjjLmsm2hBk8lE2+ZDOTZ7W62WHqY1m02dg0ql\notdz4lC1WlXNajwea9+q1arigNknOab6DGKxbbF9UbF9thd+EAS/6vv+d/q+/zSWi+IvAXgPXbIH\n4NpLtcNAlYln3xiTH6VpapiUDBQ5dd/Y2DDCnWTAms2mthmGocGvLRJFkU5OHMc6wJxg4bquTkiv\n11OTS0DIdKs7OztG4gtX3mF+FOkXRyPU63VjQXFRZDGNp9Opfs6yzDA1GRBcB1X60O12dfHO53Nd\nOLzopF/9ft+YD+n72tqakagiJj6DmZ97MpkYlL7iR+UsQuZ28TxPx5UzGXd3d3UOmKNld3dX/eLt\ndvvMkQwW2xbbFxXbwBldOr7vvwvAC0EQPArgPwbwKycuyX32r6xYOf9isW3lIstZXTpfA+B3ACAI\ngo/7vl8BUKS/bwHYfqlGnnjiCQDLXUt2uSRJVJNhM+skZSqbebKjVqtVg62PtQHRJDjRgXdpx3F0\nB+Z6o9y3Uqmk3w8GAz1pF3OSE2iKxaJqD/P53NAuZBdnzYTN/SzLDA1NElu4WhHX63Qcx2hfxi+X\nyxntcBQEH0RJfxqNhmoS0neu0TmdTlWTYapaz/PUBcAaZ7lcNlwS8n2r1dJxStNUn+no6EgPsG7c\nuKGaVZIkhhYp7TBl8GQy0WtOprq/TLHYtti+qNg+8wv/aQBvA/Drvu/fBDAC8Jzv+38mCII/APBN\nAP7ZSzXymc98BsByAMTEybJMzTCurFMul43yXyKlUkmvieNYJ7jZbOr1/X5fAVGtVo1oBCZm4u+l\nPxxhADygmWWfJ5+my2THcaxmepqmunCSJNHFxOD3PE+fezabGckx4hNsNBqG6S9tep6n1wuopD/y\nPQPIcRyjhJz8hjMMGWyn+X9lzACT96NUKhkhhnKfLMt0vLrdrr6kisWiztlJNwCPJdPMMu0uv0A5\nk/IVcIZbbFtsX1Rsn/mF/9MAft73/d87buN7sQxd+2nf9/MA/kMQBO8/c6+sWFmdWGxbubBy1kPb\nMYBvPeVPb3857chOdXh4aKRSi6nUbDZ1t8zlcmpylkolg/dCdt3Dw0PVWHinHw6Hxu7NJ/d8cs7f\ni6lbrVYNGlQ2w0UL2NnZAbDURsQEn06nhsYi/fI8T7WmKIpUg+MY7fF4rBrcwcGBHpZVKhXVABzH\nOTVhg1PBuei04zj6/XQ6Vc1jMBjob7l4hAgfEHmeZxThED6VLMuMqIaNjQ0AJuvheDxWTYY11FKp\npL8dj8eq8W1sbBjp8jLu5XLZiHOWPsxmMzX9uejIyxWLbYvti4ptYMWZtlLRnbms+VT+ZDaiAJvD\nphaLhVH2jMuYyaCyiVosFvW3HBrnuq5+Xy6XjQQZaT+KIjVZmZNEQNXv942+yMKazWaGeS2LhblM\n2NQej8cKPvZDep6nbTqOY7gEOAROnjXLHvCKl0ol/T5NU4PbhF8wMmYyjpz56XmeYWozaKUN5oXh\nyItWq2X8VoSLOAMPfLqtVkufm18q7XZbX5TNZtNIOpJ+jkajlZOnWWxbbJ9HbFsuHStWrFi5JLJS\nDd/3fQAmC918PlcNgJkDPc8zeDRkV+QiBoVCQU/aOZUZMKvcfC6KWjGf2+227qJJkhixwmLiijkn\nfQaW2ovc/8qVK6rVMJcIm5wn2fFYw5HPm5ub+nz1el01w3K5rOPkuq5qQY1GwyiuIL/lw8Asy4wE\nEo6+YE4Q+ZeZAOX+hUJBtbDJZHKqhsqHU7VazThY46gDLv4hbVYqFUPLk7nklP1Go2EUvhZ8cFTD\nqsRi22L7PGL7XBQxZ3Kg8Xh8qvnZbDaxv78PwDRjeRHxhEynU8P0EZBxxhuHN/HAt1oto8ycLIpG\no6E+RyZp4uLITFsrbXAx6vF4bFCmCjf59va23p/5tTc2NvDss8/qfcQsZHOfucxZOBEnSRLtT6vV\nUv9gLpfTxZvP59WUFXOSKxEVCgWjSDb7iLkvskDCMDR4zfllxKXn2OyVMY3jWH2ejuPo9xwpwRWh\n+AXqOI7h116FWGxbbJ9HbFuXjhUrVqxcElmphs+xrmIu5nI5o84km3myG/OOzyYwx/ueZL8TTYl3\nRy5s7Hme/q1UKhl1SLlQBadQiybGbYpWUygUsL6+DsAsYMD0pwBOZSCMosjQHuTzbDZTjaXX6xlp\n9NKHNE2Ngyq573g81rHkNH1Or+cDJ74/m8Vcc5MjPE5jKxwOh8Y4yn1YO2NGw0KhYNANMOcLx6kz\n26OMWbPZNKJMVi0W2xbb5xHbK33hC793p9NRc9JxHAXWbDZTEC4WC4PqlH1mYgZxQkK1WlUAFYtF\nBfBJ7myu1sORE7xYZDH2+30FWaPRMLixpQ3p4/7+vgEQ9v2dRrrU6/XUzCuVSupPXSwWRsIJl2eT\nZ2KZzWYGva48x2Aw0FA3ztRkHhcOC5O+s6k7nU71pVOr1dTHyNWH2HSO49joo4wB098yx3ij0dCF\n5rqu4dpgv7YIJ/2czBrlMVuFWGxbbJ9HbFuXjhUrVqxcElmphi/CBYk5GYQPRubzue54zP5XLpeN\ngyWuAylazfPPP2+c9EsUAptftVrNqBYk7XAxhOFwqDtzkiSq8XDRB/ldkiSqzTFTH9Pfjsdj/T4M\nQz1sKpVKhjYi2sNwOFQtxHVdTXKp1WqGWciFmZlbhU1/rmQkWtloNNIxYy1JNIp+v29ocDw3TOUq\nwubqYDAwxosPBWVuuJrPfD43NBkuVMHVk2T81tfXVWMuFAo6J6sWi22Lbfksskpsr/SFL2Yp82Ww\nycYA4ko9URQZ2W9iina7XYPTWgZyOBzq5DB3RT6fN3xpXJaO/a5iFu7v7xshayIcQiafwzDURcH8\n5VzMOMsyXdz5fP7UqjYcsXB4eKjRHJubm8aCkkXEiSe8SGu1mgKeCanYRxuGoVEAWoRD8zgKRF4o\n0+lU+8gc5PV63aDllc+NRsMoEs0Ljcmj5OXI5fWm06kCPk1TnaeNjY1z4bsXsdi22D6P2LYuHStW\nrFi5JLJSDZ/5MNis4QpBbM7JTshVXzh5ZH19XQ+E+MDEcRy9PgxDo9qMaA+FQsFIwuBEB7kvF60o\nFovG4Y/cR37HJnWz2dRrB4OBaiNsfjJnSLFY1IM+jkPO5/Nq6nY6HYNfo9vtal9Ey3Mcx9AWOW6Z\nNT65vlQqGYdV0i9xBziOY5imoillWaZtsCYynU6NuGy5J2uozNvCB2ec2FKtVg2NlgtDcyQDm9Ks\nTa9CLLYtts8jtle6KgSok8lEB4YTUrggcJIkeg1nxfV6PYNj5ODgAIDpM3Mcx0iAkM+c0Xeyiows\nhF6vp+A7Sfkqv+WTe7nvYDBQk4yJoZrNpk58LpczaE/ZxGeOc6a2lYXT6/WM0DEx/ZmwibMRZ7OZ\n+lHX1tb0mslkoqY0c65wCT3pF0dhMA+667oKyCRJdCyFm0XGgHnCOftUFhSH7HHpvkqloiBnKlz+\nbRiGhsn8SjjDXw2x2LbYPo/Yti4dK1asWLkkslIN//r16wCAvb093dF5F+dTa8/zdAfmyj4cMdDv\n93XnzOVyBpWq7KL5fF53S44SSJLESH0W83I4HKoJmsvlDP4T0eLkPpzskqap/p3NMI7B9TxPP3NE\nAR/icZtJkpyaBDKfz1WTKRaLRjq5aAmz2UxNWo7CYO0hTVODZ0TGQmQ2m6mZyVEYrBHm83mjQhFH\nFHDEBDMB8mGafGa3xZUrV4zIEtZK5blPmtWrjtKx2LbYPo/YXukLX8xCLjzcaDR0IkajkZGswLSn\ncv1kMtFJjeNYB4MLRnuepwPGphJnPs7ncwUCm1YnM/3keyZjku94Ik5yYXMii5iotVrNiEBgwit5\nPvZzDgYDbG1tAViG5jE/iVxfqVQ0C7JYLOr4cYm1JEl07JvNpj5fFEXaB1l8PO5Jkmjf4zg2/LvC\nHVMqlfTFwUkozKEyHo+1X57nGX5fzkSVvnAIW6PRONW/Oh6P1VXA912VWGxbbJ9HbFuXjhUrVqxc\nElmphv+pT30KgFnc+cqVK7r77e7uGgdeXCBZDoe4KAEXhgYeaFnVatXg4GAzT+KQmduEIx9O7uRs\nVotGIG0Xi0WjKpFcO5vNjKQV1iq4cLJIlmWGtiFjcJIbRJ51MpmoBseVjvL5vDLuVatVbG8va29H\nUYTNzU0Ay0Mujj8WrYKpXHd3d/XvYuLP53PtexiGBicJF+fgQz9O/mFuEJm/xWKhmmSj0dD24zjW\nsXRd19BW5b5cNWqxWKycHtli22L7PGJ7pS98oUYNw1CBsre3p0karuvqwDDnBPsBC4WCEXUgIVy1\nWk1NscViYZhBnOQifrJWq6XmYj6f18lhEy2OY104cRxrf+Q7DrHiTErP8wxucJ485ssW4SSRKIr0\nBSDPC5i0tI1Gw1iMHIom49dut3Hv3j0ASx+i8LWvra0ZhZaZs0PaE+HIi36/bySwPPfccwCAmzdv\nar84iYjHvdVq6Zyx2c3Zhd1uV8eEM1ErlYrxEhKf7slxPRmZ8sUWi22L7fOIbevSsWLFipVLIivV\n8EVjSZLEiFdlDUc0EKZY5cQQZtZbLBa6E9brdf08mUwMzUpM2n6/r/G0TCGbpqlqLax95XI53Y2z\nLNP2OY72tCLSjuPo/afTqT4TJ4w4jmOkakubXEt0sVioxjKdTvUaLmrBMdphGOqYXb16FXfu3AGw\n1D7f8IY3AFhqcNI+m96cnMNJM6eZ/b1eD3t7ezq+csA0Ho91vLhwg+u6RoQK12HlpBLWELkOKVPI\nsmbM0TCrjtKx2LbYPo/YPheZtoVCwUgSkcHg8DPOhGPfH2f0ra+va5hZtVrVxRJFkbbJVWu49Fqn\n0zF4L2TCuWSa53nqN8zlchoyJ21zSN1JalbOquSsOTGTuYiyXCfPzYtFXh5cIJkXHZNvsa+v0Wjo\nAvnEJz6h9L2PPfaYMfacTSn/cgk9aYND1Pr9via+HB4e6rNyBaRyuazXs985yzIjrJD9wfJMxWJR\nF1o+n1c3wHw+1/HmML1cLrfyKB2LbYvt84jtP9EL3/f9NwD41wB+LAiCH/d9/zqAXwbgALgP4N1B\nEES+7387gL8FYAHgZ4Ig+Lkz98yKlS+CWGxbuUzyki983/erAP4ZgA/Q1/8QwHuCIPg13/d/GMB3\n+b7/SwD+HoA/BSAG8Ljv+/8qCIKjz9W2HCpFUaS7IsfvcvII75xxHOv3nufpjlcul42CyrJbcvQA\nm52lUsmo6clak2gBHL1wMrWbixhIH/nvYlI7joONjQ3tIxdflp0+yzLVADgtfjabqTbFSSUcNcEH\ng5x6zVV2ABhVh5588kkAwCOPPKKaYLlcNuh75fllPlhTO1l4W9L+wzDUcZ9Op6c+H7MnzmYzbZML\nVvMzyfNKfzgShHlTZB44MubzicW2xfZFxfbnkj+Jhh8B+C8A/I/03dcC+N7jz+8F8P0AAgCPB0Ew\nAADf9/9fAF9z/PdThc1YDieTwa7X62rGuq5rmMPsJ+NsNplMBm0ul9NBjaLISOTgk3DpQxiGOjkn\nzXBOGhGR79h850kaDAa6+AqFguErFQBnWWYQUkn7w+FQJziXyxmuAgHfeDw2CkmLMMGU4zjqV261\nWupOOBm9wHzjch/pi+d5+hzVatUwSwWcxWLR8LVK37mqE0cyMP0tLxxuh3lTmGO8WCxq3zhqwnGc\nUwtfnyIW2xbbOtYXDNunyku+8IMgSAAkEup0LNUgCGR73QNwDcBVAPt0jXxvxcq5FIttK5dNXo1D\n2891gvCSJws/+qM/utqTtUsqP/ADP7DqLnypiMX2l5hYbH9+OWsc/tj3fYkN2gKwffzfVbpGvrdi\n5UtJLLatXFg56wv//fj/2zufUKuqMIr/JIgi09cfSByFEAu1kS8pB+UpAwsiBz51IBHyokkvjEoo\nnPQPCiUkGjSqF0JBRBRPi4gItElRoVIhn+JAwhBrEPYyUEQHe986PF9c83HPPtyzfqNzzr1wF5t1\nv3v23udbF9bn4/XA58C3wEpJI5Lmk9Y4v567RGMaxd42Q8u8frkMkkaB14FbgXPACWAz8C5wDXAc\n2BIR5ySNAduAC8CbEfHewJQbM0fsbdM1+hZ8Y4wxw4GzdIwxpiO44BtjTEcolqUjaRdwF2lNdGtE\nfFdQyw7gbtJ4vAo8DIwCvX8q3hkRnzasqQI+BH7Ol34EdjBL23/DusaBR2qX7gC+B64Den/f80xE\n/NCQntZFI9jbfTVVtMzbXfF1kYIvaTVwW0SskrQUeAdYVUjLvcDtWctNwAHgK+D5iNhbQlONfREx\n1juRNMmMtn/grSYFZUO9nfWsBjYCy0mbmz81qWWQ0Qhz0GRvXx6t8nZXfF1qSWcN8AlARBwGbpC0\noJCW/cCGfPwH6Rf9qv9+e1EqYCof7wHuLycFSGZ7ueDn96IR6s/EV1w6RneSoxEi4m+gF40wCOzt\nK6OiPd4eWl+XWtJZBNSnRr/la6ebFhIR5/l3yjYOfAacByYkPU1qo5+IiN+b1gYskzQF3Ai8yOxt\n/0WQtBL4JSJO5miClyTdDBwGnsoGHCgtjUawty+PVnp72H3dlk3b4m3oktaRvhQTpLWy5yLiPuAg\n8EIBSUdJX4R1wKOk6Wb9B7r0mD1Gel4d4A1gW0TcQ1pLfKKUqBlccTRCAxoaw97+Xwy1r0vd4c9s\nVV9M2ogogqS1wHbggZyIWF87m6LhdXKAiDgBfJBPj0k6Ser2vDbfZZRu76+AJwEi4uPa9T3AphKC\nMtOzjNFs0QjfDOjz7e0+tNzbFUPs61J3+F8AYwCSVgC/RsSfJYRIWgjsBB7qbXZI+kjSkvyWCmh0\n0yZr2Czp2Xy8CLgFmOTStv/GkbQYmI6Is5LmSfpS0kh+uaLAeNUoHY1gb/fX1Upvd8HXxTptJb0G\n/DNViohDhXQ8TprWHqldniRNf88A06Sd+lMN67oeeB8YAa4mTYEPALuZ0fbfpK6sbRR4JSIezOcb\nSZnyf5HiCcYj4kxDOloXjWBv99XVSm93wdeOVjDGmI7Qlk1bY4wxA8YF3xhjOoILvjHGdAQXfGOM\n6Qgu+MYY0xFc8I0xpiO44BtjTEdwwTfGmI5wEcHPa53lhydKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b53cfb518>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oyr5mTO2u1+uJ+kzvt4EqLdujKkhpZ2trK1E17ZzhcHjTVO3Nzc3ozDrJKy8d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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b53ce7c50>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Q2F0UcjSbzTAo9vb2wvqhnU4nIlYqQYbdYT5GYp9MQj2Lz/ck8dSdnZ0oFsvxRmlTu/up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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b53c64780>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    image, mask = dataset[np.random.randint(0, len(dataset))]\n",
    "    plot2x2Array(image, mask)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "C0GOxaFNo9E4",
    "outputId": "2b13ae36-34e1-47db-a9d4-6e6af2dbf577"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depth distribution')"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b53eca9b0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "plt.hist(depth['z'], bins=50)\n",
    "plt.title('Depth distribution')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KppQiJu5pyNx"
   },
   "source": [
    "Run-length encoding (RLE) is a very simple form of lossless data compression in which runs of data (that is, sequences in which the same data value occurs in many consecutive data elements) are stored as a single data value and count, rather than as the original run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "xcCzhu2Op1hr"
   },
   "outputs": [],
   "source": [
    "#convert to image\n",
    "def rleToMask(rleString,height,width):\n",
    "    #width heigh\n",
    "    rows,cols = height,width\n",
    "    try:\n",
    "        #get numbers\n",
    "        rleNumbers = [int(numstring) for numstring in rleString.split(' ')]\n",
    "        #get pairs\n",
    "        rlePairs = np.array(rleNumbers).reshape(-1,2)\n",
    "        #create an image\n",
    "        img = np.zeros(rows*cols,dtype=np.uint8)\n",
    "        #for each pair\n",
    "        for index,length in rlePairs:\n",
    "            #get the pixel value \n",
    "            index -= 1\n",
    "            img[index:index+length] = 255\n",
    "        \n",
    "        \n",
    "        #reshape\n",
    "        img = img.reshape(cols,rows)\n",
    "        img = img.T\n",
    "    \n",
    "    #else return empty image\n",
    "    except:\n",
    "        img = np.zeros((cols,rows))\n",
    "    \n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "juu-LWOevPty"
   },
   "outputs": [],
   "source": [
    "#for measuring how salty an image is \n",
    "def salt_proportion(imgArray):\n",
    "  try:\n",
    "    unique, counts = np.unique(imgArray, return_counts=True)\n",
    "    ## the Total number of pixels is 101*101 = 10,201\n",
    "    return counts[1]/10201.\n",
    "  except:\n",
    "    return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "sbfbF90DwgSd"
   },
   "outputs": [],
   "source": [
    "train_mask['mask'] = train_mask['rle_mask'].apply(lambda x: rleToMask(x, 101,101))\n",
    "train_mask['salt_proportion']= train_mask['mask'].apply(lambda x: salt_proportion(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 397
    },
    "colab_type": "code",
    "id": "EHkN512TxDLg",
    "outputId": "d833d146-a6ce-4e72-89b0-b29bf5a7f124"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>rle_mask</th>\n",
       "      <th>mask</th>\n",
       "      <th>salt_proportion</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>575d24d81d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a266a2a9df</td>\n",
       "      <td>5051 5151</td>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
       "      <td>0.504950</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75efad62c1</td>\n",
       "      <td>9 93 109 94 210 94 310 95 411 95 511 96 612 96...</td>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
       "      <td>0.993334</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34e51dba6a</td>\n",
       "      <td>48 54 149 54 251 53 353 52 455 51 557 50 659 4...</td>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
       "      <td>0.149201</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4875705fb0</td>\n",
       "      <td>1111 1 1212 1 1313 1 1414 1 1514 2 1615 2 1716...</td>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
       "      <td>0.042839</td>\n",
       "      <td>797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                                           rle_mask  \\\n",
       "0  575d24d81d                                                NaN   \n",
       "1  a266a2a9df                                          5051 5151   \n",
       "2  75efad62c1  9 93 109 94 210 94 310 95 411 95 511 96 612 96...   \n",
       "3  34e51dba6a  48 54 149 54 251 53 353 52 455 51 557 50 659 4...   \n",
       "4  4875705fb0  1111 1 1212 1 1313 1 1414 1 1514 2 1615 2 1716...   \n",
       "\n",
       "                                                mask  salt_proportion    z  \n",
       "0  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...         0.000000  843  \n",
       "1  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...         0.504950  794  \n",
       "2  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...         0.993334  468  \n",
       "3  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...         0.149201  727  \n",
       "4  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...         0.042839  797  "
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = train_mask.merge(depth, how='left')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "colab_type": "code",
    "id": "W6QjqAhxxPRg",
    "outputId": "ed785f7a-b26d-48b8-991b-3614f1fd929a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Proportion of salt v.depth')"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GQTffuYW50m5uwGlb8WbFmK9aRQEZN2e9KRcNkSCuX95a69sQ1IhS+nil1ko3zrNumQvc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keL9jdZTcOBMo2xEZ3t9ZiWrbmuPNdpqPFTN5OaoEQtOjMvUNbJfWJKBuDiPsB0tNZj58ML8eRj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\nQRAEoYJEMkEQBEEQBEGo+P+Uk9Rd2Y5qgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b51253a58>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(merged['salt_proportion'], merged['z'])\n",
    "plt.title('Proportion of salt v.depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "LU5IlBcyxk59",
    "outputId": "8cb2ba8e-bc47-467d-a545-23831aa7f8fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.10361580365557428\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation:\", np.corrcoef(merged['salt_proportion'], merged['z'])[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8WMJFjpTyJdo"
   },
   "source": [
    "\n",
    "## U-Nets\n",
    "\n",
    " *  This looks like a computer vision problem!\n",
    " *  What outperforms everything else when it comes to computer vision most of the time? (hint deep learning)\n",
    " *  Specifically, deep convolutional neural networks.\n",
    " *  AlexNet\n",
    " *  VGG-16, VGG-19;\n",
    " *  Inception Nets;\n",
    " *  ResNet;\n",
    " *  Squeeze Net\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "its high to low res, then low res to high res\n",
    "\n",
    "    The first part (encoder) is where we apply convolutional blocks followed by a maxpool downsampling to encode the input image into feature representations at multiple different levels.\n",
    "\n",
    "convolutions→ downsampling.\n",
    "\n",
    "Example\n",
    "\n",
    "##### a sample down block\n",
    "def make_conv_bn_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1):\n",
    "    return [\n",
    "        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,  stride=stride, padding=padding, bias=False),\n",
    "        nn.BatchNorm2d(out_channels),\n",
    "        nn.ReLU(inplace=True)\n",
    "    ]\n",
    "self.down1 = nn.Sequential(\n",
    "    *make_conv_bn_relu(in_channels, 64, kernel_size=3, stride=1, padding=1 ),\n",
    "    *make_conv_bn_relu(64, 64, kernel_size=3, stride=1, padding=1 ),\n",
    ")\n",
    "\n",
    "##### convolutions followed by a maxpool\n",
    "down1 = self.down1(x)\n",
    "out1   = F.max_pool2d(down1, kernel_size=2, stride=2)\n",
    "\n",
    "    The second part (decoder) of the network consists of upsample and concatenation followed by regular convolution operations\n",
    "\n",
    "upsampling → concatenation →convolutions.\n",
    "\n",
    "##### a sample up block\n",
    "def make_conv_bn_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1):\n",
    "    return [\n",
    "        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,  stride=stride, padding=padding, bias=False),\n",
    "        nn.BatchNorm2d(out_channels),\n",
    "        nn.ReLU(inplace=True)\n",
    "    ]\n",
    "self.up4 = nn.Sequential(\n",
    "    *make_conv_bn_relu(128,64, kernel_size=3, stride=1, padding=1 ),\n",
    "    *make_conv_bn_relu(64,64, kernel_size=3, stride=1, padding=1 )\n",
    ")\n",
    "self.final_conv = nn.Conv2d(32, num_classes, kernel_size=1, stride=1, padding=0 )\n",
    "\n",
    "##### upsample out_last, concatenate with down1 and apply conv operations\n",
    "out   = F.upsample(out_last, scale_factor=2, mode='bilinear')  \n",
    "out   = torch.cat([down1, out], 1)\n",
    "out   = self.up4(out)\n",
    "\n",
    "##### final 1x1 conv for predictions\n",
    "final_out = self.final_conv(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from keras.models import Model, load_model\n",
    "from keras.layers import Input\n",
    "from keras.layers.core import Lambda, RepeatVector, Reshape\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, ReduceLROnPlateau\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "9CqHUAH1zdmX"
   },
   "outputs": [],
   "source": [
    "im_width =128\n",
    "im_height =128\n",
    "border = 5\n",
    "im_chan=2 #Number of channels:first is original and second cumsum(axis =0)\n",
    "n_features =1 # Numbers of extra features, like depth\n",
    "#path_train = '../input/train/'\n",
    "#path_test = '../input/test/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1496
    },
    "colab_type": "code",
    "id": "0IIJHLVj0MUb",
    "outputId": "0eeff2e9-be1f-4550-9e6d-ea9ed942b939"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img (InputLayer)                (None, 128, 128, 2)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 128, 128, 8)  152         img[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, 8)    1160        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 32, 32, 8)    0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 32, 32, 8)    584         max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 32, 32, 8)    584         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 16, 16, 8)    0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 16, 16, 8)    584         max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "feat (InputLayer)               (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 16, 8)    584         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_1 (RepeatVector)  (None, 64, 1)        0           feat[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 8, 8, 8)      0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 8, 8, 1)      0           repeat_vector_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 8, 8, 9)      0           max_pooling2d_4[0][0]            \n",
      "                                                                 reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 8, 8, 128)    10496       concatenate_1[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_2 (Concatenate)     (None, 16, 16, 72)   0           conv2d_transpose_1[0][0]         \n",
      "                                                                 conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 16, 16, 64)   41536       concatenate_2[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_3 (Concatenate)     (None, 32, 32, 40)   0           conv2d_transpose_2[0][0]         \n",
      "                                                                 conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 32, 32, 32)   11552       concatenate_3[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_4 (Concatenate)     (None, 64, 64, 24)   0           conv2d_transpose_3[0][0]         \n",
      "                                                                 conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 64, 16)   3472        concatenate_4[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_5 (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_5[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: 313,929\n",
      "Trainable params: 313,929\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Build U-Net model\n",
    "input_img = Input((im_height, im_width, im_chan), name='img')\n",
    "input_features = Input((n_features, ), name='feat')\n",
    "\n",
    "c1 = Conv2D(8,(3,3), activation='relu', padding='same') (input_img)\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(8,(3,3),activation= 'relu', padding= 'same') (c2)\n",
    "p2 = MaxPooling2D((2,2)) (c2)\n",
    "\n",
    "c3 = Conv2D(8,(3,3), activation='relu', padding='same') (p2)\n",
    "c3 = Conv2D(8,(3,3),activation= 'relu', padding= 'same') (c3)\n",
    "p3 = MaxPooling2D((2,2)) (c3)\n",
    "\n",
    "c4 = Conv2D(8,(3,3), activation='relu', padding='same') (p3)\n",
    "c4 = Conv2D(8,(3,3),activation= 'relu', padding= 'same') (c4)\n",
    "p4 = MaxPooling2D((2,2)) (c4)\n",
    "\n",
    "#join features information in the depthest layer\n",
    "f_repeat =RepeatVector(8*8)(input_features)\n",
    "f_conv = Reshape((8,8, n_features))(f_repeat)\n",
    "p4_feat = concatenate([p4, f_conv], -1)\n",
    "\n",
    "c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (p4_feat)\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",
    "#check out this skip connection thooooo\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=[input_img, input_features], outputs=[outputs])\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy') #, metrics=[mean_iou]) # The mean_iou metrics seens to leak train and test values...\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 662
    },
    "colab_type": "code",
    "id": "1XPY1ti_36Xa",
    "outputId": "17fc962a-cc05-40f1-a1d7-178bb0a796f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets) (4.6.0)\n",
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      "Requirement already satisfied: html5lib!=1.0b1,!=1.0b2,!=1.0b3,!=1.0b4,!=1.0b5,!=1.0b6,!=1.0b7,!=1.0b8,>=0.99999999pre in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (1.0.1)\n",
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     ]
    }
   ],
   "source": [
    "!pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "mpXyu0874MPF",
    "outputId": "07452ebc-04a0-4fc8-fc23-0552e0524cf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing train images and masks ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4000/4000 [00:21<00:00, 183.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from tqdm import tqdm\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from skimage.transform import resize\n",
    "\n",
    "train_ids = next(os.walk(train_path+\"images\"))[2]\n",
    "\n",
    "#Get and Resize train images and masks\n",
    "X= np.zeros((len(train_ids), im_height, im_width, im_chan),dtype=np.float32)\n",
    "y = np.zeros((len(train_ids),im_height,im_width,1), dtype=np.float32)\n",
    "X_feat = np.zeros((len(train_ids),n_features),dtype=np.float32)\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\n",
    "  \n",
    "  \n",
    "    #Depth\n",
    "    #X_feat[n] =depth.loc[id_.replace('.png',''),'z']\n",
    "  \n",
    "    #Load X\n",
    "    img = load_img(path + '/images/' + id_, grayscale =True)\n",
    "    x_img = img_to_array(img)\n",
    "    x_img = resize(x_img, (128, 128, 1), mode='constant', preserve_range=True)\n",
    "    \n",
    "    # Create cumsum x\n",
    "    x_center_mean = x_img[border:-border, border:-border].mean()\n",
    "    x_csum = (np.float32(x_img)-x_center_mean).cumsum(axis=0)\n",
    "    x_csum -= x_csum[border:-border, border:-border].mean()\n",
    "    x_csum /= max(1e-3, x_csum[border:-border, border:-border].std())\n",
    "\n",
    "    # Load Y\n",
    "    mask = img_to_array(load_img(path + '/masks/' + id_, grayscale=True))\n",
    "    mask = resize(mask, (128, 128, 1), mode='constant', preserve_range=True)\n",
    "\n",
    "    # Save images\n",
    "    X[n, ..., 0] = x_img.squeeze() / 255\n",
    "    X[n, ..., 1] = x_csum.squeeze()\n",
    "    y[n] = mask / 255\n",
    "\n",
    "print('Done!')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "7EFKDJAt7J89"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_valid, X_feat_train, X_feat_valid, y_train, y_valid = train_test_split(X,X_feat, y, test_size=0.15, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 627
    },
    "colab_type": "code",
    "id": "_tG4BHAg8ken",
    "outputId": "8f37584d-d3d2-4705-a1a3-274b4d711881"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3400 samples, validate on 600 samples\n",
      "Epoch 1/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.19152, saving model to model-tgs-salt-1.h5\n",
      "Epoch 2/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.19152 to 0.19152, saving model to model-tgs-salt-1.h5\n",
      "Epoch 3/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.19152\n",
      "Epoch 4/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-10.\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.19152\n",
      "Epoch 5/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.19152\n",
      "Epoch 6/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.19152\n",
      "Epoch 7/50\n",
      "3400/3400 [==============================] - 15s 4ms/step - loss: 0.1562 - val_loss: 0.1915\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-11.\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.19152\n",
      "Epoch 00007: early stopping\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    EarlyStopping(patience=5, verbose=1),\n",
    "    ReduceLROnPlateau(patience=3, verbose=1),\n",
    "    ModelCheckpoint('model-tgs-salt-1.h5', verbose=1, save_best_only=True, save_weights_only=True)\n",
    "]\n",
    "\n",
    "results = model.fit({'img': X_train, 'feat': X_feat_train}, y_train, batch_size=16, epochs=50, callbacks=callbacks,\n",
    "                    validation_data=({'img': X_valid, 'feat': X_feat_valid}, y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "7TFi2Z_F-ELj"
   },
   "outputs": [],
   "source": []
  }
 ],
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