Salt_Identification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Salt Identification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "jiPIWA5AmASl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "3d6365f1-3e5d-44b2-941f-04f97f7d19d0"
      },
      "cell_type": "code",
      "source": [
        "# Install dependencies\n",
        "\n",
        "!pip install imageio\n",
        "!pip install torch\n",
        "!pip install kaggle\n",
        "!pip install pywidgets\n",
        "\n",
        "\n",
        "# http://pytorch.org/\n",
        "from os import path\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "\n",
        "accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision\n",
        "import torch"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (2.4.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imageio) (4.0.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from imageio) (1.14.5)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->imageio) (0.45.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (0.4.1)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.4.7.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2018.8.24)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.2.6)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.25.0)\n",
            "Requirement already satisfied: urllib3<1.23.0,>=1.15 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: Unidecode>=0.04.16 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.0.22)\n",
            "Collecting pywidgets\n",
            "\u001b[31m  Could not find a version that satisfies the requirement pywidgets (from versions: )\u001b[0m\n",
            "\u001b[31mNo matching distribution found for pywidgets\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BOFE9BhBmEFE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Import Dependencies\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import imageio\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import torch\n",
        "from torch.utils import data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3VgxkhIRmIYj",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "5ffdbf29-0b13-4fd6-cd3a-80482d7d2e00"
      },
      "cell_type": "code",
      "source": [
        "# Upload the kaggle dataset into google colab\n",
        "\n",
        "from google.colab import files \n",
        "uploaded = files.upload()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f3fe80aa-531f-4a00-826a-1d6707cc8b91\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f3fe80aa-531f-4a00-826a-1d6707cc8b91\">\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": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_AnCjb7JmS3U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dfcc1f53-3a24-417f-8bed-9ffd71444632"
      },
      "cell_type": "code",
      "source": [
        "# Is it there ?\n",
        "\n",
        "!ls -lha kaggle.json"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 68 Sep 12 11:16 kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jHu9wl5qpfi2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# File configuration\n",
        "\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fE0NYLOtp1ib",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "b8728bcb-bfd3-4cca-e621-f25b91bad5ff"
      },
      "cell_type": "code",
      "source": [
        " # Download our dataset\n",
        "\n",
        "!kaggle competitions download -c tgs-salt-identification-challenge"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading depths.csv to /content\n",
            "\r  0% 0.00/322k [00:00<?, ?B/s]\n",
            "100% 322k/322k [00:00<00:00, 64.4MB/s]\n",
            "Downloading sample_submission.csv to /content\n",
            "  0% 0.00/264k [00:00<?, ?B/s]\n",
            "100% 264k/264k [00:00<00:00, 78.6MB/s]\n",
            "Downloading train.csv to /content\n",
            "  0% 0.00/922k [00:00<?, ?B/s]\n",
            "100% 922k/922k [00:00<00:00, 87.6MB/s]\n",
            "Downloading test.zip to /content\n",
            " 97% 159M/163M [00:01<00:00, 105MB/s] \n",
            "100% 163M/163M [00:01<00:00, 151MB/s]\n",
            "Downloading train.zip to /content\n",
            " 45% 17.0M/37.9M [00:00<00:00, 52.8MB/s]\n",
            "100% 37.9M/37.9M [00:00<00:00, 116MB/s] \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TmZ-6GA0vJkv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136102
        },
        "outputId": "0003f24a-7449-4272-ae9a-4292c61ca93f"
      },
      "cell_type": "code",
      "source": [
        "# Unzip\n",
        "\n",
        "!ls\n",
        "!unzip train.zip"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "depths.csv   sample_data\t    test.zip   train.zip\n",
            "kaggle.json  sample_submission.csv  train.csv\n",
            "Archive:  train.zip\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "plLELHnt-kx7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Let's create a class to represent our dataset\n",
        "\n",
        "class TGSSaltDataset(data.Dataset):\n",
        "  \n",
        "  def __init__(self, root_path, file_list):\n",
        "    self.root_path = root_path\n",
        "    self.file_list = file_list\n",
        "    \n",
        "  def __len__(self):\n",
        "    return len(file_list)\n",
        "  \n",
        "  def __getitem__(self, index):\n",
        "    file_id = self.file_list[index]\n",
        "    \n",
        "    #image folder and image path\n",
        "    image_folder = os.path.join(self.root_path, 'images')\n",
        "    image_path = os.path.join(image_folder, file_id +'.png')\n",
        "    \n",
        "    #label folder and label path\n",
        "    mask_folder = os.path.join(self.root_path, 'masks')\n",
        "    mask_path = os.path.join(mask_folder, file_id + '.png')\n",
        "    \n",
        "    #read them\n",
        "    image = np.array(imageio.imread(image_path), dtype = np.uint8)\n",
        "    mask = np.array(imageio.imread(mask_path), dtype = np.uint8)\n",
        "    \n",
        "    return image, mask\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uw4NS09OC1hh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#init our class dataset\n",
        "\n",
        "train_mask = pd.read_csv('train.csv')\n",
        "depth = pd.read_csv('depths.csv')\n",
        "\n",
        "train_path = './'\n",
        "\n",
        "file_list = list(train_mask['id'].values)\n",
        "dataset = TGSSaltDataset(train_path, file_list)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ArbA6s9YEMN9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Function to vizualise images\n",
        "\n",
        "def plot2x2array(image, mask):\n",
        "  \n",
        "  #invoke the 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",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sjsfJ0RrETj3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1262
        },
        "outputId": "b9e60242-4511-45a0-b273-10fbdd5699b2"
      },
      "cell_type": "code",
      "source": [
        "#print the images\n",
        "\n",
        "for i in range(5):\n",
        "  image, mask = dataset[np.random.randint(0, len(dataset))]\n",
        "  plot2x2array(image, mask)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C/WUOuY5+27uc/nulwTH8icMOIZgo7BBCCKEP6KnCbmUks0evvZ7t/YyyRDWhWrBjYhe0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\nWe1oRRI4Ppx+OWs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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd430dd8cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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355H7oX+OrKrQj3cAYAfEbzu8UoA9rsRGP2Q0g8Qhfccz8KxZQeAZ0Q5H3iVW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\nPtMXlIWPuRMR29+JHfRr37t94OSzts8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SjN3S008/nXPPPTdf+9rXmiU2N5Rr\nuVu68MILc8011+Tb3/52jj766Jx44onDyr5p06bl9ttvz9q1a7N06dKsWLFiWNmXrCsDOm/evCTr\nSoZus802mTRpUu68884kw8PGpLdyfA/H33JSv+cXquH+e+6V33IyeL/nrqYmPf/883PnnXdm1KhR\n+fSnP5299tqrW6YkSa6++upcdNFFzdrISfK5z30up59++nq5lrutiy66KOPHj8+0adNy6qmnDhv7\nvvWtb2X27NlJkg9+8IOZMmXKsLJv+fLlmTVrVp544ok899xz+fCHP5y+vr6cccYZWbt2bfbdd998\n8pOf3KQ2/SHk+B5uv+Wkfs+DoeH8ex6Ov+VkaH/PlUu8VCqVSqUeUGU6K5VKpVKpB1R/sEulUqlU\n6gHVH+xSqVQqlXpA9Qe7VCqVSqUeUP3BLpVKpVKpB1R/sEulUqlU6gHVH+xSqVQqlXpA9Qe7VCqV\nSqUe0P8DUGHE095HkwAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd430a81e10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CPCVpmGI2Zc2K2pS2rTfs+GYPK3mbWtCcbZd63RxQZdfByt3OY8o+Utw89RR5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\neXWyU33YvD1fL+MpbW8EgtW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YsmVLjyKsho15r/Fdi+8yoPe5v9T6+1yL7zJQ2fe5ah/s\nnTt3Yty4cT3ztVCveOjQoRgxYgQAoLm5GZdeemnRWsvV5MEHH8TixYt75mvJvl//+tfo7u7GzTff\njKamJrS0tNSUfQBwzTXX4P3338fs2bOxYMECLFq0CGPGjOlZXw0b6+rqerqIGXmq8V2L7zKg97m/\n1Pr7XIvvMlDZ97mqPuyjKdRQOvhrr72G5uZmfPOb38ScOXN6llfbxhdeeAHnn38+PvShDxVdX237\nAKCtrQ2PP/443n//fdxwww3H2FQL9r344ouYMGECnn76afz85z/HLbfcgtGjR/esrwUbGc+mWrQV\nqD279D6XTi2/z3l8l4H+vc9V+2AXq1fc0NBQLXN6eOONN/D1r38dTz31FEaPHl201nK1WLduHTZv\n3ox169Zh69atGDZsWE3ZV19fjwsuuAB1dXWYNGkSRo4ciaFDh9aMfQCwceNGTJ8+HQBw9tlnY//+\n/T2dvQDUhI1Avmp81+q7DOh97g+1/j7n5V0Gyvc+V21I/JJLLumpYfyzn/0MjY2NGDVqVLXMAQDs\n3bsXy5Ytwze+8Q2MHTsWQPFay9XikUcewXPPPYfvf//7mD9/PhYuXFhT9k2fPh1vvfUWjhw5gtbW\nVnR2dtaUfQBw+umnY9OmTQCALVu2YOTIkTjzzDOxYcMGALVhI5CvGt+1+C4Dep/7S62/z3l5l4Hy\nvc9VLU360EMPYcOGDRgyZAi+/OUv4+yzz66WKQCAVatWYeXKlTjjjDN6lj3wwANYunTpcbWWq83K\nlSsxceJETJ8+HXfeeWfN2Pe9730Pzc3NAIC//uu/xpQpU2rKvo6ODixZsgS7du3CoUOH8MUvfhEN\nDQ24++67ceTIEZx33nn4u7/7uwG16USo8V1r7zKg97kc1PL7XIvvMlDZ91m1xIUQQogcoEpnQggh\nRA7QB1sIIYTIAfpgCyGEEDlAH2whhBAiB+iDLYQQQuQAfbCFEEKIHKAPthBCCJED9MEWQgghcsD/\nA00EoJQ+iMH1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd430a06b00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y5sprnXf6/FoRTSPYOYd6DXW1Qavj6eqFrspo9zDt5qWePNHIfF2l0Kj2KDqc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\nXV0dxo0bhz59+qC6uhp33nk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rrSLFrljaHUsrjKnC5vOaR011xtej/tv6WJWuqi9VmTpP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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd430a23d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhC6gIxQ2o7ztXc+YMUPSYP9rq40q95XR1MzHppdbRXW3UrKGStrwM9Vn1VOZ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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd430972828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DyWhXn_gE9Mj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "a9dca956-9ae8-4013-8aa9-da3a48680419"
      },
      "cell_type": "code",
      "source": [
        "#plot the distributions of depths\n",
        "\n",
        "plt.figure(figsize = (6,6))\n",
        "plt.hist(depth ['z'], bins = 50)\n",
        "plt.title('Depth Distribution')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'Depth Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mqKGhQW63W4sXL5bD4VB+fr5ycnIUEhKiCRMmKDU1taNLD7r6+npde+21mjJl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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd430dd8c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "U2GHnn-wMCGP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DVDOHDnyOYpM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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",
        "    \n",
        "    except: \n",
        "        return 0.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Si22LsawSKZJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A5sdMnUVSOBk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "119c0af7-1643-4984-84b9-c2047d7a3705"
      },
      "cell_type": "code",
      "source": [
        "#merge depths\n",
        "\n",
        "merged = train_mask.merge(depth, how = 'left')\n",
        "merged.head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "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  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "id": "ZZAxQdklY3f6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "62a6b40d-2b39-4028-cfbe-1ad665027377"
      },
      "cell_type": "code",
      "source": [
        "#show proportions of salt vs depth\n",
        "\n",
        "plt.figure(figsize = (12, 6))\n",
        "plt.scatter(merged['salt_proportion'], merged['z'])\n",
        "plt.title('Proportion of salt v. depth')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'Proportion of salt v. depth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3E9hSl8kHmmgUyuq2af0cWCxmtHd40O9LDTVUjoFCx7qP9Sw6epQMh82Cxjo3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\nShbsDgE8eyMKs6y7CONMOOiH3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WJCRo+o3jmVIRaFUzJ6QHwzES3NIBHIvZsX4/pgFG+/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89o5e7Nx7WrGS2d2rZmFl6xQkUxk89R9/kvWbbKgJ4LGv3gyvkNG1NubPZx5RqXVdkVsvpIf+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\nEskEQRAEQRAEIYFEMkEQBEEQBEFI+P8YppGWLE8yrQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd41ecd0358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KjUbjhvXY9Mq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "95a93f46-b242-4852-dad4-65c34b1df81d"
      },
      "cell_type": "code",
      "source": [
        "#just how correlated is it ?\n",
        "\n",
        "print(\"Correlation: \", np.corrcoef(merged['salt_proportion'], merged['z'])[0, 1])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Correlation:  0.10361580365557428\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q1O96xkcvnOW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "U-net CNN\n",
        "\n",
        "This model is for unet \n",
        "it is used for segmenting an image inside an image , it is an auto-encoder\n",
        "vgg is ued for classifing many images \n",
        "\n",
        "- Convolution\n",
        "- pooling\n",
        "- activation function\n",
        "\n",
        "How does it work ?\n",
        "\n",
        "-encode\n",
        "-decode\n",
        "-skip connection"
      ]
    },
    {
      "metadata": {
        "id": "Osim6cgIvzsg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "36qPZZeUi8HG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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 # Number of extra features, like depth\n",
        "#path_train = '../input/train/'\n",
        "#path_test = '../input/test/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p_0Z7pp6jtVy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1445
        },
        "outputId": "fe547f24-e446-446d-d865-c0aaee5570be"
      },
      "cell_type": "code",
      "source": [
        "#Build U-net\n",
        "\n",
        "input_img = Input((im_height, im_width, im_chan), name='img')\n",
        "# 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(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",
        "# 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",
        "#decoder\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()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "img (InputLayer)                (None, 128, 128, 2)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 128, 128, 8)  152         img[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 128, 128, 8)  584         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2D)  (None, 64, 64, 8)    0           conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 64, 64, 16)   1168        max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 64, 64, 16)   2320        conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, 32, 32, 16)   0           conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 32, 32, 32)   4640        max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 16, 16, 32)   0           conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 16, 16, 64)   18496       max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "feat (InputLayer)               (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "repeat_vector_2 (RepeatVector)  (None, 64, 1)        0           feat[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling2D) (None, 8, 8, 64)     0           conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "reshape_2 (Reshape)             (None, 8, 8, 1)      0           repeat_vector_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_6 (Concatenate)     (None, 8, 8, 65)     0           max_pooling2d_11[0][0]           \n",
            "                                                                 reshape_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 8, 8, 128)    75008       concatenate_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 8, 8, 128)    147584      conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_5 (Conv2DTrans (None, 16, 16, 64)   32832       conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_7 (Concatenate)     (None, 16, 16, 128)  0           conv2d_transpose_5[0][0]         \n",
            "                                                                 conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 16, 16, 64)   73792       concatenate_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_6 (Conv2DTrans (None, 32, 32, 32)   8224        conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 32, 32, 64)   0           conv2d_transpose_6[0][0]         \n",
            "                                                                 conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 32, 32, 32)   18464       concatenate_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_7 (Conv2DTrans (None, 64, 64, 16)   2064        conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 64, 64, 32)   0           conv2d_transpose_7[0][0]         \n",
            "                                                                 conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 64, 64, 16)   4624        concatenate_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 64, 64, 16)   2320        conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_8 (Conv2DTrans (None, 128, 128, 8)  520         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_10 (Concatenate)    (None, 128, 128, 16) 0           conv2d_transpose_8[0][0]         \n",
            "                                                                 conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 128, 128, 8)  1160        concatenate_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 128, 128, 8)  584         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 128, 128, 1)  9           conv2d_45[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 486,897\n",
            "Trainable params: 486,897\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xze4QPhc8o7V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "outputId": "5a4c4b32-7b54-4e28-f32c-a86758cc9187"
      },
      "cell_type": "code",
      "source": [
        "!pip install ipywidgets"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.6/dist-packages (7.4.1)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.6/dist-packages (from ipywidgets) (4.6.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets) (4.4.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.4.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets) (3.4.1)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.6/dist-packages (from ipywidgets) (4.3.2)\n",
            "Requirement already satisfied: ipython>=4.0.0; python_version >= \"3.3\" in /usr/local/lib/python3.6/dist-packages (from ipywidgets) (5.5.0)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets) (4.5.3)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets) (5.2.3)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets) (4.4.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets) (0.2.0)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets) (2.6.0)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.6/dist-packages (from widgetsnbextension~=3.4.0->ipywidgets) (5.2.2)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.3.1->ipywidgets) (4.3.0)\n",
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            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets) (39.1.0)\n",
            "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",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets) (2.1.3)\n",
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            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets) (16.0.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets) (2.5.3)\n",
            "Requirement already satisfied: terminado>=0.3.3; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (0.8.1)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (5.4.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (2.10)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets) (0.6.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets) (0.1.7)\n",
            "Requirement already satisfied: mistune>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (0.8.3)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (0.5.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (1.4.2)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (0.3.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (0.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (2.1.4)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (1.0)\n",
            "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",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from html5lib!=1.0b1,!=1.0b2,!=1.0b3,!=1.0b4,!=1.0b5,!=1.0b6,!=1.0b7,!=1.0b8,>=0.99999999pre->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DO06cQXFaf3q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d87cedb9-176f-40cb-e2fb-05958e69b433"
      },
      "cell_type": "code",
      "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",
        "\n",
        "train_ids = next(os.walk(train_path+\"images\"))[2]\n",
        "\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",
        "    # 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!')"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Getting and resizing train images and masks ... \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 4000/4000 [00:19<00:00, 202.96it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "DpAan4mTsPxw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "E5ONG8rZ2kJh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "5ea4f03d-d6bb-4738-8117-2709200f6c95"
      },
      "cell_type": "code",
      "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))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3400 samples, validate on 600 samples\n",
            "Epoch 1/50\n",
            "3400/3400 [==============================] - 276s 81ms/step - loss: 0.2024 - val_loss: 0.2315\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.23146, saving model to model-tgs-salt-1.h5\n",
            "Epoch 2/50\n",
            "1824/3400 [===============>..............] - ETA: 2:01 - loss: 0.1883"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}