{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "U-net.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "S52QO1B2OCE8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-KqbpC0ODDh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mv kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibKiyR6EODLn",
        "colab_type": "code",
        "outputId": "5d02c970-d93f-41e7-cce5-cea60c734397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "!kaggle datasets download -d mateuszbuda/lgg-mri-segmentation"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading lgg-mri-segmentation.zip to /content\n",
            " 98% 329M/334M [00:04<00:00, 56.0MB/s]\n",
            "100% 334M/334M [00:05<00:00, 70.1MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3K5ZM3cQP_54",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -q lgg-mri-segmentation.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3axwrWDdSBO6",
        "colab_type": "code",
        "outputId": "e94a994b-eaec-48d5-ee90-3725afd90bce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!pip install -q tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 332.1MB 52kB/s \n",
            "\u001b[K     |████████████████████████████████| 3.0MB 26.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 419kB 52.8MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNG7r6QTSP7E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-IpwIcGSRg0",
        "colab_type": "code",
        "outputId": "f5399cad-2ed5-4e79-e530-c88c01e3dc8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-alpha0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMlgsYHxSTmj",
        "colab_type": "code",
        "outputId": "1a85b14c-a7d6-414f-a558-ee9d05ae735a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.python.keras.utils.data_utils import Sequence\n",
        "\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, LearningRateScheduler\n",
        "\n",
        "from keras.preprocessing import image\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from skimage.transform import resize\n",
        "\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import numpy as np\n",
        "import os\n",
        "import shutil\n",
        "import PIL\n",
        "import pathlib\n",
        "import pandas as pd\n",
        "import cv2\n",
        "from imgaug import augmenters as iaa\n",
        "import imgaug as ia\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
        "import re"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVSBBul4avmX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv kaggle_3m/README.md ./"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUKZ0Y26a2rt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv kaggle_3m/data.csv ./"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwIeCLEfQ9SV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "patients_folders = os.listdir(\"kaggle_3m\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qhy7CiU2aXXD",
        "colab_type": "code",
        "outputId": "25c1bd51-27ed-4278-f8c9-be0140eb1cae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(patients_folders)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-ZLb-Dwa_db",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfOyk5NLbFF2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir files/masks && mkdir files/images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkSsyXZPbay7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for folder in patients_folders:\n",
        "  files = os.listdir(f'kaggle_3m/{folder}')\n",
        "  \n",
        "  for file in sorted(files):\n",
        "    file_url = f'kaggle_3m/{folder}/{file}'\n",
        "    \n",
        "    if \"mask\" in file:\n",
        "      shutil.move(file_url, \"files/masks\")\n",
        "    else:\n",
        "      shutil.move(file_url, \"files/images\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIG2jpS4evag",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images_paths = os.listdir(\"files/images\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsMAT0pVjgJw",
        "colab_type": "code",
        "outputId": "a8760a15-0e37-43b3-ffcc-a3c2add84b67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(images_paths)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3929"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWL1u4czjCKo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "val_size = int(len(images_paths) * .20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxiL1Btgjdkb",
        "colab_type": "code",
        "outputId": "d14bcad7-b9fd-4d0f-e972-ff306268ea77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "val_size"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "785"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXYcsapmjAGR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = images_paths[val_size:]\n",
        "X_val = images_paths[:val_size]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Adzdkflajn_F",
        "colab_type": "code",
        "outputId": "5aeef120-699f-41c4-fc26-3d9af7c4e421",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(X_train), len(X_val)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3144, 785)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQOYNeviBI_O",
        "colab_type": "text"
      },
      "source": [
        "## Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXH9qTYgBIi5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DataGenerator(Sequence):\n",
        "    def __init__(self, images_paths, batch_size=32, validation=False, predict=False, shuffle=True):\n",
        "      self.batch_size = batch_size\n",
        "      self.images_paths = images_paths\n",
        "      self.shuffle = shuffle\n",
        "      self.validation = validation\n",
        "      self.predict = predict\n",
        "      self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "      return int(np.floor(len(self.images_paths) / self.batch_size))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "      from_range = index * self.batch_size\n",
        "      to_range = (index + 1) * self.batch_size\n",
        "      indexes = self.indexes[from_range:to_range]\n",
        "      \n",
        "      if self.predict:\n",
        "        X = self.__load_predict__(indexes)\n",
        "        return X\n",
        "      \n",
        "      else:\n",
        "        X, y = self.__load__(indexes)\n",
        "        return X, y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "      self.indexes = np.arange(len(self.images_paths))\n",
        "      \n",
        "      if self.shuffle == True:\n",
        "          np.random.shuffle(self.indexes)\n",
        "    \n",
        "    def __load_image__(self, index):\n",
        "      image_path = self.images_paths[index]\n",
        "  \n",
        "      image_loaded = image.load_img(f'files/images/{image_path}')\n",
        "      image_loaded = image.img_to_array(image_loaded)\n",
        "      image_loaded = resize(image_loaded, (input_shape), mode='constant', preserve_range=True)\n",
        "      \n",
        "      image_loaded = image_loaded / 255.0\n",
        "      \n",
        "      return image_loaded\n",
        "    \n",
        "    def __load_image_and_mask__(self, index):\n",
        "      image_path = self.images_paths[index]\n",
        "      mask_path = image_path[:-4] + \"_mask.tif\"\n",
        "  \n",
        "      image_loaded = image.load_img(f'files/images/{image_path}')\n",
        "      image_loaded = image.img_to_array(image_loaded)\n",
        "      image_loaded = resize(image_loaded, (input_shape), mode='constant', preserve_range=True)\n",
        "\n",
        "      mask_loaded = image.img_to_array(image.load_img(f'files/masks/{mask_path}', color_mode=\"grayscale\"))\n",
        "      mask_loaded = resize(mask_loaded, (img_size, img_size, 1), mode='constant', preserve_range=True)\n",
        "      \n",
        "      image_loaded = image_loaded / 255.0\n",
        "      mask_loaded = mask_loaded / 255.0\n",
        "      \n",
        "      return image_loaded, mask_loaded\n",
        "    \n",
        "    def __load_predict__(self, indexes):\n",
        "      X = []\n",
        "      \n",
        "      for index in indexes:\n",
        "        img = self.__load_image__(index)\n",
        "        X.append(img)\n",
        "\n",
        "      return np.array(X)\n",
        "    \n",
        "    def __load__(self, indexes):\n",
        "      X = []\n",
        "      y = []\n",
        "      \n",
        "      for index in indexes:\n",
        "        img, mask = self.__load_image_and_mask__(index)\n",
        "        X.append(img)\n",
        "        y.append(mask)\n",
        "      \n",
        "      return np.array(X), np.array(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kb5bN6D0F5n",
        "colab_type": "text"
      },
      "source": [
        "## Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMcgHKflXCk5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def downstream_block(layer, number_filters):\n",
        "  layer = layers.Conv2D(number_filters, 3, activation='relu', padding=\"same\")(layer)\n",
        "  cropped_feature_map = layers.Conv2D(number_filters, 3, activation='relu', padding=\"same\")(layer)\n",
        "  layer = layers.MaxPool2D(2, strides=2, padding='same')(cropped_feature_map)\n",
        "  \n",
        "  return layer, cropped_feature_map"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRb4BH1tY58_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def upstream_block(layer, number_filters, cropped_feature_map):\n",
        "  layer = layers.Conv2DTranspose(number_filters, (2, 2), strides=(2, 2), padding='same')(layer)\n",
        "  layer = layers.concatenate([layer, cropped_feature_map])\n",
        "  layer = layers.Conv2D(number_filters, 3, activation='relu', padding=\"same\")(layer)\n",
        "  layer = layers.Conv2D(number_filters, 3, activation='relu', padding=\"same\")(layer)\n",
        "  \n",
        "  return layer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCr-REtVV_ce",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def u_net():\n",
        "  input = layers.Input(input_shape)\n",
        "  layer = input\n",
        "  \n",
        "  cropped_feature_maps = []\n",
        "  \n",
        "  for index in range(steps + 1):\n",
        "    current_number_filters = number_filters * 2 ** index\n",
        "    layer, cropped_feature_map = downstream_block(layer, current_number_filters)\n",
        "    cropped_feature_maps.append(cropped_feature_map)\n",
        "  \n",
        "  layer = cropped_feature_maps.pop()\n",
        "  \n",
        "  for index in range(steps - 1, -1, -1):\n",
        "    current_number_filters = number_filters * 2 ** index\n",
        "    layer = upstream_block(layer, current_number_filters, cropped_feature_maps[index])\n",
        "  \n",
        "  output = layers.Conv2D(number_classes, 1, activation='sigmoid')(layer)\n",
        "  model = Model(input, output)\n",
        "  \n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHXEzNI7b4S2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_shape = (256, 256, 3)\n",
        "img_size = 256\n",
        "number_filters = 16 \n",
        "steps = 4\n",
        "number_classes = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieKkHXSD65Kt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = None\n",
        "model = u_net()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUbxLduAAEBe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "\n",
        "learning_rate = 3e-4\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmqPPMTy_3DR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def polynomial_decay(epoch):\n",
        "\tpower = 1.0\n",
        " \n",
        "\talpha = learning_rate * (1 - (epoch / float(epochs))) ** power\n",
        "\treturn alpha"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7nC0Cah_2bf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights_name = \"epoch={epoch:02d}|accuracy={acc:.4f}|val_accuracy={val_acc:.4f}.h5\"\n",
        "\n",
        "checkpoint = ModelCheckpoint(weights_name, monitor=\"val_acc\", verbose=1, save_best_only=True,\n",
        "                                 save_weights_only=True, mode=\"max\", period=1)\n",
        "\n",
        "lr_decay = LearningRateScheduler(polynomial_decay)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbvyddbShR7N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss=\"binary_crossentropy\", optimizer=Adam(), metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtp72XcO-Hk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_steps = int(len(X_train) / batch_size)\n",
        "val_steps = int(len(X_val) / batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKdFkCAq-dPW",
        "colab_type": "code",
        "outputId": "57b023ed-f150-415b-8472-2ec492e8d88e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_steps, val_steps"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiXUJ30X-cQb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_generator = DataGenerator(X_train, batch_size)\n",
        "val_generator = DataGenerator(X_val, batch_size, validation=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYPnmkq7_4n6",
        "colab_type": "code",
        "outputId": "79017a54-9aa3-4889-e163-c5320f8057e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3417
        }
      },
      "source": [
        "trained_model = model.fit_generator(train_generator,\n",
        "                        epochs=epochs,\n",
        "                        steps_per_epoch=train_steps,\n",
        "                        callbacks=[checkpoint, lr_decay],\n",
        "                        validation_data=val_generator,\n",
        "                        validation_steps=val_steps,\n",
        "                        verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.2181 - acc: 0.9648\n",
            "Epoch 00001: val_acc improved from -inf to 0.98868, saving model to epoch=01|accuracy=0.9650|val_accuracy=0.9887.h5\n",
            "98/98 [==============================] - 66s 678ms/step - loss: 0.2163 - acc: 0.9650 - val_loss: 0.0369 - val_acc: 0.9887\n",
            "Epoch 2/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0319 - acc: 0.9890\n",
            "Epoch 00002: val_acc improved from 0.98868 to 0.98882, saving model to epoch=02|accuracy=0.9890|val_accuracy=0.9888.h5\n",
            "98/98 [==============================] - 59s 601ms/step - loss: 0.0319 - acc: 0.9890 - val_loss: 0.0287 - val_acc: 0.9888\n",
            "Epoch 3/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0289 - acc: 0.9890\n",
            "Epoch 00003: val_acc did not improve from 0.98882\n",
            "98/98 [==============================] - 59s 605ms/step - loss: 0.0288 - acc: 0.9891 - val_loss: 0.0280 - val_acc: 0.9887\n",
            "Epoch 4/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0267 - acc: 0.9890\n",
            "Epoch 00004: val_acc did not improve from 0.98882\n",
            "98/98 [==============================] - 58s 594ms/step - loss: 0.0268 - acc: 0.9890 - val_loss: 0.0273 - val_acc: 0.9885\n",
            "Epoch 5/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0252 - acc: 0.9909\n",
            "Epoch 00005: val_acc improved from 0.98882 to 0.99163, saving model to epoch=05|accuracy=0.9909|val_accuracy=0.9916.h5\n",
            "98/98 [==============================] - 59s 602ms/step - loss: 0.0252 - acc: 0.9909 - val_loss: 0.0256 - val_acc: 0.9916\n",
            "Epoch 6/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0230 - acc: 0.9923\n",
            "Epoch 00006: val_acc improved from 0.99163 to 0.99205, saving model to epoch=06|accuracy=0.9923|val_accuracy=0.9920.h5\n",
            "98/98 [==============================] - 59s 602ms/step - loss: 0.0229 - acc: 0.9923 - val_loss: 0.0234 - val_acc: 0.9920\n",
            "Epoch 7/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0220 - acc: 0.9927\n",
            "Epoch 00007: val_acc improved from 0.99205 to 0.99323, saving model to epoch=07|accuracy=0.9927|val_accuracy=0.9932.h5\n",
            "98/98 [==============================] - 59s 603ms/step - loss: 0.0220 - acc: 0.9927 - val_loss: 0.0198 - val_acc: 0.9932\n",
            "Epoch 8/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0204 - acc: 0.9932\n",
            "Epoch 00008: val_acc improved from 0.99323 to 0.99330, saving model to epoch=08|accuracy=0.9933|val_accuracy=0.9933.h5\n",
            "98/98 [==============================] - 59s 604ms/step - loss: 0.0203 - acc: 0.9933 - val_loss: 0.0190 - val_acc: 0.9933\n",
            "Epoch 9/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0176 - acc: 0.9938\n",
            "Epoch 00009: val_acc improved from 0.99330 to 0.99383, saving model to epoch=09|accuracy=0.9938|val_accuracy=0.9938.h5\n",
            "98/98 [==============================] - 59s 597ms/step - loss: 0.0176 - acc: 0.9938 - val_loss: 0.0168 - val_acc: 0.9938\n",
            "Epoch 10/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0158 - acc: 0.9943\n",
            "Epoch 00010: val_acc improved from 0.99383 to 0.99419, saving model to epoch=10|accuracy=0.9943|val_accuracy=0.9942.h5\n",
            "98/98 [==============================] - 58s 591ms/step - loss: 0.0158 - acc: 0.9943 - val_loss: 0.0156 - val_acc: 0.9942\n",
            "Epoch 11/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0143 - acc: 0.9946\n",
            "Epoch 00011: val_acc improved from 0.99419 to 0.99481, saving model to epoch=11|accuracy=0.9945|val_accuracy=0.9948.h5\n",
            "98/98 [==============================] - 59s 597ms/step - loss: 0.0143 - acc: 0.9945 - val_loss: 0.0135 - val_acc: 0.9948\n",
            "Epoch 12/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0124 - acc: 0.9950\n",
            "Epoch 00012: val_acc did not improve from 0.99481\n",
            "98/98 [==============================] - 58s 593ms/step - loss: 0.0124 - acc: 0.9950 - val_loss: 0.0135 - val_acc: 0.9946\n",
            "Epoch 13/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9953\n",
            "Epoch 00013: val_acc improved from 0.99481 to 0.99555, saving model to epoch=13|accuracy=0.9953|val_accuracy=0.9956.h5\n",
            "98/98 [==============================] - 59s 605ms/step - loss: 0.0114 - acc: 0.9953 - val_loss: 0.0110 - val_acc: 0.9956\n",
            "Epoch 14/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9956\n",
            "Epoch 00014: val_acc improved from 0.99555 to 0.99561, saving model to epoch=14|accuracy=0.9956|val_accuracy=0.9956.h5\n",
            "98/98 [==============================] - 58s 593ms/step - loss: 0.0106 - acc: 0.9956 - val_loss: 0.0106 - val_acc: 0.9956\n",
            "Epoch 15/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9960\n",
            "Epoch 00015: val_acc improved from 0.99561 to 0.99566, saving model to epoch=15|accuracy=0.9961|val_accuracy=0.9957.h5\n",
            "98/98 [==============================] - 58s 595ms/step - loss: 0.0093 - acc: 0.9961 - val_loss: 0.0103 - val_acc: 0.9957\n",
            "Epoch 16/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0085 - acc: 0.9963\n",
            "Epoch 00016: val_acc improved from 0.99566 to 0.99601, saving model to epoch=16|accuracy=0.9963|val_accuracy=0.9960.h5\n",
            "98/98 [==============================] - 58s 595ms/step - loss: 0.0085 - acc: 0.9963 - val_loss: 0.0094 - val_acc: 0.9960\n",
            "Epoch 17/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0084 - acc: 0.9963\n",
            "Epoch 00017: val_acc did not improve from 0.99601\n",
            "98/98 [==============================] - 59s 603ms/step - loss: 0.0085 - acc: 0.9963 - val_loss: 0.0097 - val_acc: 0.9959\n",
            "Epoch 18/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.9967\n",
            "Epoch 00018: val_acc improved from 0.99601 to 0.99634, saving model to epoch=18|accuracy=0.9967|val_accuracy=0.9963.h5\n",
            "98/98 [==============================] - 58s 595ms/step - loss: 0.0076 - acc: 0.9967 - val_loss: 0.0085 - val_acc: 0.9963\n",
            "Epoch 19/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.9969\n",
            "Epoch 00019: val_acc improved from 0.99634 to 0.99638, saving model to epoch=19|accuracy=0.9969|val_accuracy=0.9964.h5\n",
            "98/98 [==============================] - 59s 606ms/step - loss: 0.0068 - acc: 0.9969 - val_loss: 0.0083 - val_acc: 0.9964\n",
            "Epoch 20/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.9970\n",
            "Epoch 00020: val_acc improved from 0.99638 to 0.99654, saving model to epoch=20|accuracy=0.9970|val_accuracy=0.9965.h5\n",
            "98/98 [==============================] - 60s 608ms/step - loss: 0.0066 - acc: 0.9970 - val_loss: 0.0083 - val_acc: 0.9965\n",
            "Epoch 21/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.9971\n",
            "Epoch 00021: val_acc did not improve from 0.99654\n",
            "98/98 [==============================] - 59s 602ms/step - loss: 0.0063 - acc: 0.9971 - val_loss: 0.0082 - val_acc: 0.9965\n",
            "Epoch 22/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.9971\n",
            "Epoch 00022: val_acc did not improve from 0.99654\n",
            "98/98 [==============================] - 59s 598ms/step - loss: 0.0063 - acc: 0.9971 - val_loss: 0.0088 - val_acc: 0.9962\n",
            "Epoch 23/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.9973\n",
            "Epoch 00023: val_acc improved from 0.99654 to 0.99669, saving model to epoch=23|accuracy=0.9973|val_accuracy=0.9967.h5\n",
            "98/98 [==============================] - 59s 602ms/step - loss: 0.0057 - acc: 0.9973 - val_loss: 0.0079 - val_acc: 0.9967\n",
            "Epoch 24/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.9973\n",
            "Epoch 00024: val_acc improved from 0.99669 to 0.99678, saving model to epoch=24|accuracy=0.9973|val_accuracy=0.9968.h5\n",
            "98/98 [==============================] - 59s 599ms/step - loss: 0.0057 - acc: 0.9973 - val_loss: 0.0074 - val_acc: 0.9968\n",
            "Epoch 25/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.9974\n",
            "Epoch 00025: val_acc did not improve from 0.99678\n",
            "98/98 [==============================] - 60s 608ms/step - loss: 0.0053 - acc: 0.9974 - val_loss: 0.0081 - val_acc: 0.9967\n",
            "Epoch 26/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.9975\n",
            "Epoch 00026: val_acc did not improve from 0.99678\n",
            "98/98 [==============================] - 58s 593ms/step - loss: 0.0050 - acc: 0.9975 - val_loss: 0.0078 - val_acc: 0.9966\n",
            "Epoch 27/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.9976\n",
            "Epoch 00027: val_acc did not improve from 0.99678\n",
            "98/98 [==============================] - 58s 595ms/step - loss: 0.0049 - acc: 0.9976 - val_loss: 0.0079 - val_acc: 0.9967\n",
            "Epoch 28/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.9976\n",
            "Epoch 00028: val_acc improved from 0.99678 to 0.99685, saving model to epoch=28|accuracy=0.9976|val_accuracy=0.9968.h5\n",
            "98/98 [==============================] - 58s 593ms/step - loss: 0.0047 - acc: 0.9976 - val_loss: 0.0075 - val_acc: 0.9968\n",
            "Epoch 29/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.9977\n",
            "Epoch 00029: val_acc improved from 0.99685 to 0.99685, saving model to epoch=29|accuracy=0.9977|val_accuracy=0.9969.h5\n",
            "98/98 [==============================] - 59s 600ms/step - loss: 0.0045 - acc: 0.9977 - val_loss: 0.0076 - val_acc: 0.9969\n",
            "Epoch 30/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.9978\n",
            "Epoch 00030: val_acc improved from 0.99685 to 0.99689, saving model to epoch=30|accuracy=0.9978|val_accuracy=0.9969.h5\n",
            "98/98 [==============================] - 59s 603ms/step - loss: 0.0044 - acc: 0.9978 - val_loss: 0.0074 - val_acc: 0.9969\n",
            "Epoch 31/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.9978\n",
            "Epoch 00031: val_acc improved from 0.99689 to 0.99694, saving model to epoch=31|accuracy=0.9978|val_accuracy=0.9969.h5\n",
            "98/98 [==============================] - 60s 608ms/step - loss: 0.0042 - acc: 0.9978 - val_loss: 0.0075 - val_acc: 0.9969\n",
            "Epoch 32/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.9979\n",
            "Epoch 00032: val_acc did not improve from 0.99694\n",
            "98/98 [==============================] - 59s 603ms/step - loss: 0.0041 - acc: 0.9979 - val_loss: 0.0076 - val_acc: 0.9969\n",
            "Epoch 33/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.9979\n",
            "Epoch 00033: val_acc improved from 0.99694 to 0.99695, saving model to epoch=33|accuracy=0.9979|val_accuracy=0.9969.h5\n",
            "98/98 [==============================] - 59s 600ms/step - loss: 0.0039 - acc: 0.9979 - val_loss: 0.0076 - val_acc: 0.9969\n",
            "Epoch 34/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.9979\n",
            "Epoch 00034: val_acc did not improve from 0.99695\n",
            "98/98 [==============================] - 58s 592ms/step - loss: 0.0039 - acc: 0.9979 - val_loss: 0.0083 - val_acc: 0.9969\n",
            "Epoch 35/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.9980\n",
            "Epoch 00035: val_acc improved from 0.99695 to 0.99699, saving model to epoch=35|accuracy=0.9980|val_accuracy=0.9970.h5\n",
            "98/98 [==============================] - 58s 594ms/step - loss: 0.0037 - acc: 0.9980 - val_loss: 0.0078 - val_acc: 0.9970\n",
            "Epoch 36/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.9981\n",
            "Epoch 00036: val_acc did not improve from 0.99699\n",
            "98/98 [==============================] - 58s 595ms/step - loss: 0.0036 - acc: 0.9981 - val_loss: 0.0081 - val_acc: 0.9970\n",
            "Epoch 37/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.9981\n",
            "Epoch 00037: val_acc improved from 0.99699 to 0.99701, saving model to epoch=37|accuracy=0.9981|val_accuracy=0.9970.h5\n",
            "98/98 [==============================] - 59s 600ms/step - loss: 0.0036 - acc: 0.9981 - val_loss: 0.0080 - val_acc: 0.9970\n",
            "Epoch 38/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.9981\n",
            "Epoch 00038: val_acc did not improve from 0.99701\n",
            "98/98 [==============================] - 59s 602ms/step - loss: 0.0034 - acc: 0.9981 - val_loss: 0.0082 - val_acc: 0.9970\n",
            "Epoch 39/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.9981\n",
            "Epoch 00039: val_acc improved from 0.99701 to 0.99702, saving model to epoch=39|accuracy=0.9981|val_accuracy=0.9970.h5\n",
            "98/98 [==============================] - 59s 604ms/step - loss: 0.0034 - acc: 0.9981 - val_loss: 0.0081 - val_acc: 0.9970\n",
            "Epoch 40/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.9981\n",
            "Epoch 00040: val_acc improved from 0.99702 to 0.99704, saving model to epoch=40|accuracy=0.9981|val_accuracy=0.9970.h5\n",
            "98/98 [==============================] - 58s 593ms/step - loss: 0.0034 - acc: 0.9981 - val_loss: 0.0077 - val_acc: 0.9970\n",
            "Epoch 41/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.9982\n",
            "Epoch 00041: val_acc did not improve from 0.99704\n",
            "98/98 [==============================] - 59s 604ms/step - loss: 0.0033 - acc: 0.9982 - val_loss: 0.0084 - val_acc: 0.9970\n",
            "Epoch 42/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.9982\n",
            "Epoch 00042: val_acc did not improve from 0.99704\n",
            "98/98 [==============================] - 59s 599ms/step - loss: 0.0032 - acc: 0.9982 - val_loss: 0.0083 - val_acc: 0.9970\n",
            "Epoch 43/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.9982\n",
            "Epoch 00043: val_acc improved from 0.99704 to 0.99709, saving model to epoch=43|accuracy=0.9983|val_accuracy=0.9971.h5\n",
            "98/98 [==============================] - 59s 603ms/step - loss: 0.0031 - acc: 0.9983 - val_loss: 0.0082 - val_acc: 0.9971\n",
            "Epoch 44/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.9983\n",
            "Epoch 00044: val_acc did not improve from 0.99709\n",
            "98/98 [==============================] - 59s 599ms/step - loss: 0.0030 - acc: 0.9983 - val_loss: 0.0085 - val_acc: 0.9971\n",
            "Epoch 45/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.9983\n",
            "Epoch 00045: val_acc did not improve from 0.99709\n",
            "98/98 [==============================] - 57s 585ms/step - loss: 0.0030 - acc: 0.9983 - val_loss: 0.0083 - val_acc: 0.9971\n",
            "Epoch 46/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.9983\n",
            "Epoch 00046: val_acc did not improve from 0.99709\n",
            "98/98 [==============================] - 59s 603ms/step - loss: 0.0030 - acc: 0.9983 - val_loss: 0.0082 - val_acc: 0.9971\n",
            "Epoch 47/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.9983\n",
            "Epoch 00047: val_acc improved from 0.99709 to 0.99711, saving model to epoch=47|accuracy=0.9983|val_accuracy=0.9971.h5\n",
            "98/98 [==============================] - 58s 597ms/step - loss: 0.0029 - acc: 0.9983 - val_loss: 0.0085 - val_acc: 0.9971\n",
            "Epoch 48/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.9983\n",
            "Epoch 00048: val_acc improved from 0.99711 to 0.99713, saving model to epoch=48|accuracy=0.9983|val_accuracy=0.9971.h5\n",
            "98/98 [==============================] - 58s 594ms/step - loss: 0.0029 - acc: 0.9983 - val_loss: 0.0084 - val_acc: 0.9971\n",
            "Epoch 49/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.9984\n",
            "Epoch 00049: val_acc did not improve from 0.99713\n",
            "98/98 [==============================] - 60s 612ms/step - loss: 0.0028 - acc: 0.9983 - val_loss: 0.0088 - val_acc: 0.9970\n",
            "Epoch 50/50\n",
            "97/98 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.9984\n",
            "Epoch 00050: val_acc did not improve from 0.99713\n",
            "98/98 [==============================] - 58s 590ms/step - loss: 0.0028 - acc: 0.9984 - val_loss: 0.0087 - val_acc: 0.9971\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_wotCr5wbSO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_weights(\"epoch=48_accuracy=0.9983_val_accuracy=0.9971.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCM9rGfuYjR8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def print_image_and_masks(X, index):\n",
        "  image_path = X[index]\n",
        "  mask_path = image_path[:-4] + \"_mask.tif\"\n",
        "\n",
        "  image_loaded = image.load_img(f'files/images/{image_path}')\n",
        "  image_loaded = image.img_to_array(image_loaded)\n",
        "  image_loaded = resize(image_loaded, (input_shape), mode='constant', preserve_range=True)\n",
        "  \n",
        "  mask_loaded = image.img_to_array(image.load_img(f'files/masks/{mask_path}', color_mode=\"grayscale\"))\n",
        "  mask_loaded = resize(mask_loaded, (img_size, img_size, 1), mode='constant', preserve_range=True)\n",
        "\n",
        "  image_loaded = image_loaded / 255.0\n",
        "  mask_loaded = mask_loaded / 255.0\n",
        "  \n",
        "  image_array = np.expand_dims(image_loaded, axis=0)\n",
        "  \n",
        "  predictions = model.predict(image_array)\n",
        "  \n",
        "  mask_predicted = predictions[0]\n",
        "  \n",
        "  has_mask = mask_loaded.max() > 0\n",
        "\n",
        "  fig, ax = plt.subplots(1, 3, figsize=(10, 10))\n",
        "  ax[0].imshow(image_loaded)\n",
        "\n",
        "  if has_mask:\n",
        "      ax[0].contour(mask_loaded.reshape(256, 256), colors='k', levels=[0.5])\n",
        "  ax[0].set_title('Original image')\n",
        "\n",
        "  ax[1].imshow(mask_loaded.reshape(256, 256))\n",
        "  ax[1].set_title('True Mask')\n",
        "\n",
        "  ax[2].imshow(mask_predicted.reshape(256, 256), vmin=0, vmax=1)\n",
        "\n",
        "  if has_mask:\n",
        "      ax[2].contour(mask_loaded.reshape(256, 256), colors='k', levels=[0.5])\n",
        "  ax[2].set_title('Predicted Mask')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByhaMDmmZkHR",
        "colab_type": "code",
        "outputId": "5ec33439-61d2-494b-bcd4-31d527248920",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "print_image_and_masks(X_train, 0)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MQqFMJcFtV/zb8ICEyjyzfCrmXukJVo8ePXr0eGaglz7kc4liQlKIJr4YF0/2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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sa0KsFno0ZGF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "979d155c-0136-43ca-aaea-d2d16468c5a7"
      },
      "source": [
        "print_image_and_masks(X_train, 9)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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smNJkrTdUIp8nbtM7ERBw1yC8EwEvSNyJFA5fB/yk2/5J4Otv7TSnQfhqgu+F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\nDoH/A/MZ444sQLgBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IDCSZFlu7sG",
        "colab_type": "code",
        "outputId": "823a8412-8376-43f4-a85b-6592a8102656",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "print_image_and_masks(X_train, 559)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D8H3iGYAIEgRZip1sbFPrRdd14HpYsr5CVS8Xlt8EvE9V/52IvClb/pdXXWsx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J+JHj22eG2SJSIT4H8C/kNVnYpsVy9VVRH5XEuUqv4+8PvtubeCRtfE3t123f7rFtf1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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzEH2JMlvhU6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "88d875a3-f1e8-4072-9d0b-e8d3f55637b9"
      },
      "source": [
        "print_image_and_masks(X_val, 7)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SGY+j5chCCH11KkNDSJ8QiJXzhArN2tI7vjAfRWjzemx6naHiEBGsZEhvB3M7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\niKjvzAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAb4dk7K0tzm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}